import sys, platform,os
import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import display
%matplotlib inline

import plotly.offline as py
import plotly.graph_objs as go
import plotly.tools as tls
py.init_notebook_mode(connected=True)

import matplotlib
from matplotlib.pyplot import *
from numpy import *
from pandas import *

import warnings
warnings.filterwarnings('ignore')

# Set a seed for reproducibility
SEED = 7
np.random.seed(SEED)

1. Data Preparing for clinical infomations¶

#Load data and observation
pd.set_option('display.max_columns', None)
df = pd.read_csv('sc3_Training_ClinAnnotations.csv', sep = ',')
print(df.shape)
df.info()

(583, 44)
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 583 entries, 0 to 582
Data columns (total 44 columns):
 #   Column                           Non-Null Count  Dtype  
---  ------                           --------------  -----  
 0   Study                            583 non-null    object 
 1   Patient                          583 non-null    object 
 2   D_Age                            583 non-null    int64  
 3   D_Gender                         583 non-null    object 
 4   D_OS                             583 non-null    int64  
 5   D_OS_FLAG                        583 non-null    int64  
 6   D_PFS                            583 non-null    int64  
 7   D_PFS_FLAG                       583 non-null    int64  
 8   D_ISS                            563 non-null    float64
 9   PatientType                      583 non-null    object 
 10  MA_probeLevelExpFile             0 non-null      float64
 11  MA_probeLevelExpFileSamplId      0 non-null      float64
 12  MA_geneLevelExpFile              0 non-null      float64
 13  MA_geneLevelExpFileSamplId       0 non-null      float64
 14  RNASeq_transLevelExpFile         583 non-null    object 
 15  RNASeq_transLevelExpFileSamplId  583 non-null    object 
 16  RNASeq_geneLevelExpFile          583 non-null    object 
 17  RNASeq_geneLevelExpFileSamplId   583 non-null    object 
 18  WES_mutationFileMutect           583 non-null    object 
 19  WES_mutationFileStrelkaIndel     583 non-null    object 
 20  WES_mutationFileStrelkaSNV       583 non-null    object 
 21  RNASeq_mutationFileMutect        0 non-null      float64
 22  RNASeq_mutationFileStrelkaIndel  0 non-null      float64
 23  RNASeq_mutationFileStrelkaSNV    0 non-null      float64
 24  RNASeq_FusionFile                0 non-null      float64
 25  CYTO_predicted_feature_01        583 non-null    int64  
 26  CYTO_predicted_feature_02        583 non-null    int64  
 27  CYTO_predicted_feature_03        583 non-null    int64  
 28  CYTO_predicted_feature_04        0 non-null      float64
 29  CYTO_predicted_feature_05        583 non-null    int64  
 30  CYTO_predicted_feature_06        583 non-null    int64  
 31  CYTO_predicted_feature_07        0 non-null      float64
 32  CYTO_predicted_feature_08        583 non-null    int64  
 33  CYTO_predicted_feature_09        0 non-null      float64
 34  CYTO_predicted_feature_10        237 non-null    float64
 35  CYTO_predicted_feature_11        0 non-null      float64
 36  CYTO_predicted_feature_12        583 non-null    int64  
 37  CYTO_predicted_feature_13        583 non-null    int64  
 38  CYTO_predicted_feature_14        583 non-null    int64  
 39  CYTO_predicted_feature_15        583 non-null    int64  
 40  CYTO_predicted_feature_16        583 non-null    int64  
 41  CYTO_predicted_feature_17        583 non-null    int64  
 42  CYTO_predicted_feature_18        583 non-null    int64  
 43  HR_FLAG                          583 non-null    object 
dtypes: float64(14), int64(18), object(12)
memory usage: 200.5+ KB

## Detect the full empty columns
df_na=[feature for feature in df.columns if df[feature].isnull().sum()>0]
df_na

['D_ISS',
 'MA_probeLevelExpFile',
 'MA_probeLevelExpFileSamplId',
 'MA_geneLevelExpFile',
 'MA_geneLevelExpFileSamplId',
 'RNASeq_mutationFileMutect',
 'RNASeq_mutationFileStrelkaIndel',
 'RNASeq_mutationFileStrelkaSNV',
 'RNASeq_FusionFile',
 'CYTO_predicted_feature_04',
 'CYTO_predicted_feature_07',
 'CYTO_predicted_feature_09',
 'CYTO_predicted_feature_10',
 'CYTO_predicted_feature_11']

#keep the rest data
pd.set_option('display.max_columns', None)
df_keep=df.drop(df_na,axis=1)

# clean the columns which have same values
nunique = df_keep.nunique()
cols_to_drop = nunique[nunique == 1].index
print("The column have only one value:",cols_to_drop)
df_clean=df_keep.drop(cols_to_drop, axis=1)

The column have only one value: Index(['Study', 'PatientType', 'RNASeq_transLevelExpFile',
       'RNASeq_geneLevelExpFile'],
      dtype='object')

#clean the columns that don't give so much informations
df_allClean=df_clean.drop(['Patient','RNASeq_transLevelExpFileSamplId', 'WES_mutationFileMutect','WES_mutationFileStrelkaIndel','WES_mutationFileStrelkaSNV'], axis=1)

#check the label of HR_FLAG
df_allClean['HR_FLAG'].value_counts()

FALSE       261
CENSORED    191
TRUE        131
Name: HR_FLAG, dtype: int64

df_censored=df_allClean[df_allClean['HR_FLAG']=="CENSORED"]

#check the number of patient who we categorize to high risk, OS and PFS need to be more than 18 months
df_nonHR_samples = df_censored[
 (df_censored['D_OS'] > (365 / 12) * 18) &
 (df_censored['D_PFS'] > (365 / 12) * 18)
]
df_nonHR_samples

#the patients have high risks of die, is the OS and PFS more than 18 months,
#the patients have low risks of die, is the OS or PFS less than 18 months,

df_allClean.loc[df_allClean['HR_FLAG'].eq('CENSORED')& (df_allClean.D_OS > (365 / 12) * 18) &
 (df_allClean.D_PFS > (365 / 12) * 18), 'HR_FLAG'] = 'FALSE'
df_allClean.loc[df_allClean['HR_FLAG'].eq('CENSORED')&(df_allClean.D_OS < (365 / 12) * 18) |
 (df_allClean.D_PFS < (365 / 12) * 18), 'HR_FLAG'] = 'TRUE'
df_allClean

df_allClean['HR_FLAG'].value_counts()

TRUE     321
FALSE    262
Name: HR_FLAG, dtype: int64

2. Data Pre-processing for RNAseq data¶

df_mmrf = pd.read_csv('MMRF_CoMMpass_IA9_E74GTF_Salmon_entrezID_TPM_hg19.csv', sep = ',',index_col=0)
df_mmrf

# The vale is transformed to log10(X+1) for normalization and 
#than combine the clinical class label with RNAseq data
df_log=np.log10( df_mmrf+1 )
df_mmrf_T = df_log.T
df_forMM=df_allClean[['RNASeq_geneLevelExpFileSamplId', 'HR_FLAG']]
df_forMM.set_index(['RNASeq_geneLevelExpFileSamplId'],inplace=True)
df_merged = df_mmrf_T.join(df_forMM)
df_merged

#observation of data and see the reason of miss value
print((df_merged[["HR_FLAG"]].isna().sum().sum()))
print(df_merged.index.unique().value_counts().sum())

151
734

#fill all Nah to 0
df_merged['HR_FLAG'].fillna(0,inplace=True)

#transform the label to value, false(low HR to die) is 0, high HR is 1
df_merged.loc[df_merged.HR_FLAG =="FALSE", 'HR_FLAG'] = 0
df_merged.loc[df_merged.HR_FLAG =="TRUE", 'HR_FLAG'] = 1

#export the data, for Randomforest in R
#df_merged.to_csv("df_preprocessing_and_merged.csv",sep=",")

## 3. Test of different machine learning models

from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV,RandomizedSearchCV
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.neural_network import MLPClassifier
from sklearn import model_selection
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn import linear_model
from sklearn.ensemble import AdaBoostClassifier,BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.linear_model import Lasso
from sklearn.metrics import r2_score
from sklearn.linear_model import LassoCV # LassoCV
from sklearn.model_selection import StratifiedShuffleSplit 
from sklearn.feature_selection import SelectFromModel
import time

#separate the class label and our data and then separeted to train and test data
X=df_merged.loc[:, df_merged.columns != 'HR_FLAG']
Y=df_merged.iloc[:,-1]
Y=Y.astype('int')
X_train_sc, X_test_sc, y_train_sc, y_test_sc =train_test_split(X,Y,
                                                   test_size=0.1,
                                                   random_state=0)

#The different model for testing the most suitable model by seeing the different score
#but attention Adaboost and bagging model may take more time, if not interested we can skip it by deleting 
clfs = {
    'RF': RandomForestClassifier(n_estimators=70, random_state=1),
    'KNN' : KNeighborsClassifier(n_neighbors=7, weights='uniform'),
    'ADA' : AdaBoostClassifier(n_estimators=50, random_state=1),
    'BAG' : BaggingClassifier(n_estimators=50) ,
    'NB' : GaussianNB(),
    'CART' : DecisionTreeClassifier(criterion='gini'),
    'ID3' : DecisionTreeClassifier(criterion='entropy'),
    'ST' : DecisionTreeClassifier(max_depth=1), #decisionStump
    'MLP' : MLPClassifier(hidden_layer_sizes=(40,20), random_state=1, activation='logistic', alpha= 0.0001, learning_rate='constant', solver='adam')
}

#tableau de données qui peuvent nous aider à visualiser les résultats
def run_classifiers(clfs, X, Y):
    dico = {'classifier':[],'accuracy_mean':[],'accuracy_sd':[],
            'precision_mean':[], 'precision_sd':[], 'recall_mean' : [], 'recall_sd' : [],
            'AUC':[], 'time_s':[]} #output into dictionnary
    kf = KFold(n_splits=5, shuffle=True, random_state=0)
    for clf_id in clfs:
        initime = time.time()
        clf = clfs[clf_id]
        cvAccur = cross_val_score(clf, X, Y, cv=kf, scoring='accuracy')
        end = time.time()
        cvPrecision = cross_val_score(clf, X, Y, cv=kf, scoring='precision')
        cvRecall = cross_val_score(clf, X, Y, cv=kf, scoring='recall')
        cvAUC = cross_val_score(clf, X, Y, cv=kf, scoring='roc_auc')
        dico['classifier'].append(clf_id)
        dico['accuracy_mean'].append(round(np.mean(cvAccur),3))
        dico['accuracy_sd'].append(round(np.std(cvAccur),3))
        dico['precision_mean'].append(round(np.mean(cvPrecision),3))
        dico['precision_sd'].append(round(np.std(cvPrecision),3))
        dico['recall_mean'].append(round(np.mean(cvRecall),3))
        dico['recall_sd'].append(round(np.std(cvRecall),3))
        dico['AUC'].append(round(np.mean(cvAUC),3))
        dico['time_s'].append(round((end-initime),3))
    return dico

dd = run_classifiers(clfs, X_train_sc,  y_train_sc)
pd.DataFrame.from_dict(dd)

#the function for the visualization of model result, 
#!!!!!!!!!!!quite time consuming by presenting barplot with plotly!!!!!!!!!!!!!

def GetBasedModel():
    basedModels = []
    basedModels.append(('RF', RandomForestClassifier(n_estimators=70, random_state=1)))
    basedModels.append(('KNN' , KNeighborsClassifier(n_neighbors=7, weights='uniform')))
    basedModels.append(('ADA' , AdaBoostClassifier(n_estimators=50, random_state=1)))
    basedModels.append(('BAG' , BaggingClassifier(n_estimators=50)))
    basedModels.append(('NB' , GaussianNB()))
    basedModels.append(('CART' , DecisionTreeClassifier(criterion='gini')))
    basedModels.append(('ID3' , DecisionTreeClassifier(criterion='entropy')))
    basedModels.append(('ST' , DecisionTreeClassifier(max_depth=1)))
    basedModels.append(('MLP' , MLPClassifier(hidden_layer_sizes=(40,20), random_state=1, activation='logistic', alpha= 0.0001, learning_rate='constant', solver='adam')))

    
    return basedModels
    
def BasedLine2(X_train, y_train,models,score):
    # Test options and evaluation metric
    num_folds = 10
    scoring = score

    results = []
    names = []
    for name, model in models:
        kfold = StratifiedKFold(n_splits=num_folds, random_state=SEED)
        cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)
        results.append(cv_results)
        names.append(name)
        msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
        print(msg)
        
    return names, results
    
class PlotBoxR(object):
    
    
    def __Trace(self,nameOfFeature,value): 
    
        trace = go.Box(
            y=value,
            name = nameOfFeature,
            marker = dict(
                color = 'rgb(0, 128, 128)',
            )
        )
        return trace

    def PlotResult(self,names,results):
        
        data = []

        for i in range(len(names)):
            data.append(self.__Trace(names[i],results[i]))


        py.iplot(data)

#result of accuracy

models = GetBasedModel()
names,results = BasedLine2(X_train_sc, y_train_sc,models,'accuracy')
PlotBoxR().PlotResult(names,results)

RF: 0.625758 (0.067437)
KNN: 0.578788 (0.036990)
ADA: 0.619697 (0.038000)
BAG: 0.634848 (0.047165)
NB: 0.546970 (0.071854)
CART: 0.575758 (0.057095)
ID3: 0.592424 (0.045176)
ST: 0.590909 (0.042316)
MLP: 0.571212 (0.006943)

#the result of precision

names,results = BasedLine2(X_train_sc, y_train_sc,models,'precision')
PlotBoxR().PlotResult(names,results)

RF: 0.629337 (0.152403)
KNN: 0.511206 (0.060104)
ADA: 0.564012 (0.045079)
BAG: 0.606457 (0.077044)
NB: 0.473056 (0.077197)
CART: 0.510023 (0.071090)
ID3: 0.531683 (0.054541)
ST: 0.519929 (0.036070)
MLP: 0.000000 (0.000000)

#the result of recall
names,results = BasedLine2(X_train_sc, y_train_sc,models,'recall')
PlotBoxR().PlotResult(names,results)

RF: 0.339039 (0.082551)
KNN: 0.374138 (0.062757)
ADA: 0.516256 (0.042994)
BAG: 0.424138 (0.081616)
NB: 0.540764 (0.110965)
CART: 0.490640 (0.081238)
ID3: 0.495197 (0.071574)
ST: 0.734236 (0.106194)
MLP: 0.000000 (0.000000)

# the result of roc_auc
names,results = BasedLine2(X_train_sc, y_train_sc,models,'roc_auc')
PlotBoxR().PlotResult(names,results)

RF: 0.661174 (0.071689)
KNN: 0.593965 (0.048401)
ADA: 0.636081 (0.068903)
BAG: 0.681373 (0.037890)
NB: 0.548346 (0.075081)
CART: 0.539416 (0.049119)
ID3: 0.569656 (0.042586)
ST: 0.608121 (0.037385)
MLP: 0.496552 (0.010345)

# in consequences, we'd like to apply random forest to our model for the selection of features

# choose the top10 best features with randomforest
feat_labels=X_train_sc.columns[0:-1]
forest=RandomForestClassifier(n_estimators=10000,n_jobs=-1,random_state=0)
forest.fit(X_train_sc,y_train_sc)
importances=forest.feature_importances_
indices=np.argsort(importances)[::-1]

idx_thirds = importances.argsort()[-3]
thresholds = importances[idx_thirds] + 0.01

idx_features_ = (-importances).argsort()[:20]
name_features_ = np.array(X.columns)[idx_features_]
print('Selected features: {}'.format(name_features_))

Selected features: [26863 26871 26869 100169761 100169755 100169767 100169759 100169753
 100169754 100169764 6084 100169756 100169763 100169757 100169751
 100169768 100169760 100169765 101928600 692205]

#Top10 features gene result choosen by Randomforest
#input	    name 	                symbol	alias (first 5)	HGNC
#101928600	long intergenic non-protein coding RNA 2108	LINC02108		HGNC:52963
#100169751	RNA, 5S ribosomal 1	RNA5S1	RN5S1	HGNC:34362
#100169761	RNA, 5S ribosomal 10	RNA5S10	RN5S10	HGNC:34371
#100169763	RNA, 5S ribosomal 12	RNA5S12	RN5S12	HGNC:34373
#100169764	RNA, 5S ribosomal 13	RNA5S13	RN5S13	HGNC:34374
#100169765	RNA, 5S ribosomal 14	RNA5S14	RN5S14	HGNC:34375
#100169767	RNA, 5S ribosomal 16	RNA5S16	RN5S16	HGNC:34377
#100169768	RNA, 5S ribosomal 17	RNA5S17	RN5S17	HGNC:34378
#100169753	RNA, 5S ribosomal 2	RNA5S2	RN5S2	HGNC:34363
#100169754	RNA, 5S ribosomal 3	RNA5S3	RN5S3	HGNC:34364
#100169755	RNA, 5S ribosomal 4	RNA5S4	RN5S4	HGNC:34365
#100169756	RNA, 5S ribosomal 5	RNA5S5	RN5S5	HGNC:34366
#100169757	RNA, 5S ribosomal 6	RNA5S6	RN5S6	HGNC:34367
#100169759	RNA, 5S ribosomal 8	RNA5S8	RN5S8	HGNC:34369
#100169760	RNA, 5S ribosomal 9	RNA5S9	RN5S9	HGNC:34370
#26871	RNA, U1 small nuclear 1	RNU1-1	HSD1, HU1-1, RNU1, RNU1A, RNU1A3	HGNC:10120
#26869	RNA, U1 small nuclear 3	RNU1-3	HSD4, RNU1G3	HGNC:10130
#26863	RNA, variant U1 small nuclear 18	RNVU1-18	RNU1-25, RNU1-25P, RNU1-5, RNU1P1, RNU1P9	HGNC:37496
#6084	RNA, Ro60-associated Y1	RNY1	HY1, Y1	HGNC:10242
#692205	small nucleolar RNA, C/D box 89	SNORD89	HBII-289	HGNC:32750

# Give the best prediction by chosing the best parameter
parRF ={
    'n_estimators' : [0,100]
}
grid_RF = model_selection.GridSearchCV(RandomForestClassifier(), parRF, n_jobs=4, scoring='accuracy')

print("RandomForestClassifier")
grid_RF.fit(X_train_sc, y_train_sc)
print(grid_RF.best_params_)
print(grid_RF.best_score_)

RandomForestClassifier
{'n_estimators': 100}
0.6348484848484849

# HERE I DO PREDICTION with test set
meth = RandomForestClassifier(n_estimators=100, random_state=1)
meth.fit( X_train_sc, y_train_sc)
predicted = meth.predict(X_train_sc)

#prediction en fonction de resultat
individus = X_train_sc.index.values
d_predict = pd.DataFrame(predicted, index=individus, columns=["predicted"])
d_predict
#d_predict.to_csv("predict_result.csv",sep=",")

/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 531, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_forest.py", line 348, in fit
    self._validate_estimator()
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_base.py", line 134, in _validate_estimator
    raise ValueError("n_estimators must be greater than zero, "
ValueError: n_estimators must be greater than zero, got 0.

  warnings.warn("Estimator fit failed. The score on this train-test"
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 531, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_forest.py", line 348, in fit
    self._validate_estimator()
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_base.py", line 134, in _validate_estimator
    raise ValueError("n_estimators must be greater than zero, "
ValueError: n_estimators must be greater than zero, got 0.

  warnings.warn("Estimator fit failed. The score on this train-test"
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 531, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_forest.py", line 348, in fit
    self._validate_estimator()
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_base.py", line 134, in _validate_estimator
    raise ValueError("n_estimators must be greater than zero, "
ValueError: n_estimators must be greater than zero, got 0.

  warnings.warn("Estimator fit failed. The score on this train-test"
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 531, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_forest.py", line 348, in fit
    self._validate_estimator()
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_base.py", line 134, in _validate_estimator
    raise ValueError("n_estimators must be greater than zero, "
ValueError: n_estimators must be greater than zero, got 0.

  warnings.warn("Estimator fit failed. The score on this train-test"
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 531, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_forest.py", line 348, in fit
    self._validate_estimator()
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_base.py", line 134, in _validate_estimator
    raise ValueError("n_estimators must be greater than zero, "
ValueError: n_estimators must be greater than zero, got 0.

  warnings.warn("Estimator fit failed. The score on this train-test"
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 531, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_forest.py", line 348, in fit
    self._validate_estimator()
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_base.py", line 134, in _validate_estimator
    raise ValueError("n_estimators must be greater than zero, "
ValueError: n_estimators must be greater than zero, got 0.

  warnings.warn("Estimator fit failed. The score on this train-test"
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 531, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_forest.py", line 348, in fit
    self._validate_estimator()
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_base.py", line 134, in _validate_estimator
    raise ValueError("n_estimators must be greater than zero, "
ValueError: n_estimators must be greater than zero, got 0.

  warnings.warn("Estimator fit failed. The score on this train-test"
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 531, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_forest.py", line 348, in fit
    self._validate_estimator()
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_base.py", line 134, in _validate_estimator
    raise ValueError("n_estimators must be greater than zero, "
ValueError: n_estimators must be greater than zero, got 0.

  warnings.warn("Estimator fit failed. The score on this train-test"
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 531, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_forest.py", line 348, in fit
    self._validate_estimator()
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_base.py", line 134, in _validate_estimator
    raise ValueError("n_estimators must be greater than zero, "
ValueError: n_estimators must be greater than zero, got 0.

  warnings.warn("Estimator fit failed. The score on this train-test"
/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py:548: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/model_selection/_validation.py", line 531, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_forest.py", line 348, in fit
    self._validate_estimator()
  File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/sklearn/ensemble/_base.py", line 134, in _validate_estimator
    raise ValueError("n_estimators must be greater than zero, "
ValueError: n_estimators must be greater than zero, got 0.

  warnings.warn("Estimator fit failed. The score on this train-test"

# By doing some bibliography, we try the lasso model

# the score with different parameter for choosing the best
X_train_lasso, X_test_lasso, y_train_lasso, y_test_lasso =train_test_split(X,Y,random_state=0)
result=pd.DataFrame(columns=["parameter","lasso train model score","lasso test model score"])
for i in range(1,100):
    alpha=i/10
    lasso=Lasso(alpha=alpha,max_iter=10000)
    lasso.fit(X_train_lasso,y_train_lasso)
    result=result.append([{"parameter":alpha,"lasso train model score":
                           r2_score(y_train_lasso,lasso.predict(X_train_lasso)),
                           "lasso test model score":
                           r2_score(y_test_lasso,lasso.predict(X_test_lasso))
                           }])
    
result

#visualization
plt.style.use("fivethirtyeight")
%matplotlib inline

fig= plt.subplots(figsize=(15,5))
plt.plot(result["parameter"],result["lasso train model score"],label="lasso train model score")#画折线图
plt.plot(result["parameter"],result["lasso test model score"],label="lasso test model score")
plt.rcParams.update({'font.size': 15})
plt.legend()
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.xlabel("parameter",fontsize=15)
plt.ylabel("scoring",fontsize=15)

Text(0, 0.5, 'scoring')

#Since the performance of model is not very good, so we are not going to do the prediction but juste the selection of features

#lasso cross validation
 
lassocv = LassoCV(alphas=np.logspace(-3.5,-0.5,500), fit_intercept=False, positive=True, cv=5)
# cv=StratifiedShuffleSplit(n_splits=10, test_size=0.2)
lassocv.fit(X_train_lasso, y_train_lasso)

 
alpha_df = DataFrame({"alpha":lassocv.alphas_, "mse":lassocv.mse_path_.mean(1)})
best_alpha = alpha_df[alpha_df.mse == alpha_df.mse.max()].alpha
 
matplotlib.pyplot.figure(figsize=(3,2))
axvline(best_alpha.values[0])
plot(lassocv.alphas_, lassocv.mse_path_.mean(1))

[<matplotlib.lines.Line2D at 0x7fa9dfa1e460>]

alpha = best_alpha.values[0]
lasso = Lasso(alpha=alpha)
 
y_pred_lasso = lasso.fit(X_train_lasso, y_train_lasso).predict(X_test_lasso)
r2_score_lasso = r2_score(y_test_lasso, y_pred_lasso)
print(lasso)
print("r^2 on test data : %f" % r2_score_lasso)

Lasso(alpha=0.00031622776601683794)
r^2 on test data : -0.529822

#apply the best parameter
lasso = Lasso(alpha=10**(-3))
model_lasso = lasso.fit(X_train_lasso, y_train_lasso)
coef = pd.Series(model_lasso.coef_,index=X_train_lasso.columns)
print(coef[coef != 0].abs().sort_values(ascending = False))

129790    0.263364
3693      0.255492
9120      0.232740
81888     0.228643
4232      0.215346
            ...   
4674      0.000625
79094     0.000449
170626    0.000228
54979     0.000156
26871     0.000002
Length: 464, dtype: float64

clf = LassoCV().fit(X, Y)
importance = np.abs(clf.coef_)
print(importance)

[0. 0. 0. ... 0. 0. 0.]

# choose the features
idx_third = importance.argsort()[-3]
threshold = importance[idx_third] + 0.01

idx_features = (-importance).argsort()[:10]
name_features = np.array(X.columns)[idx_features]
print('Selected features: {}'.format(name_features))

Selected features: [26863 1667 10170 91663 4160 8531 2487 8653 9834 24141]

# Top10 features gene result choosen by lasso 
#input	    name 	                    symbol	alias (first 5)	HGNC
#8653	DEAD-box helicase 3 Y-linked	DDX3Y	DBY	HGNC:2699
#1667	defensin alpha 1	DEFA1	DEF1, DEFA2, HNP-1, HP-1, HP1	HGNC:2761
#10170	dehydrogenase/reductase 9	DHRS9	3-alpha-HSD, 3ALPHA-HSD, RDH-E2, RDH-TBE, RDH15	HGNC:16888
#9834	family with sequence similarity 30 member A	FAM30A	C14orf110, HSPC053, KIAA0125	HGNC:19955
#2487	frizzled related protein	FRZB	FRE, FRITZ, FRP-3, FRZB-1, FRZB-PEN	HGNC:3959
#24141	lysosomal associated membrane protein family member 5	LAMP5	BAD-LAMP, BADLAMP, C20orf103, LAMP-5, UNC-46	HGNC:16097
#4160	melanocortin 4 receptor	MC4R	BMIQ20	HGNC:6932
#91663	myeloid associated differentiation marker	MYADM	SB135	HGNC:7544
#26863	RNA, variant U1 small nuclear 18	RNVU1-18	RNU1-25, RNU1-25P, RNU1-5, RNU1P1, RNU1P9	HGNC:37496
#8531	Y-box binding protein 3	YBX3	CSDA, CSDA1, DBPA, ZONAB	HGNC:2428

print("="*40, "System Information", "="*40)
uname = platform.uname()
print(f"System: {uname.system}")
print(f"Node Name: {uname.node}")
print(f"Release: {uname.release}")
print(f"Version: {uname.version}")
print(f"Machine: {uname.machine}")
print(f"Processor: {uname.processor}")

======================================== System Information ========================================
System: Darwin
Node Name: Xinyue
Release: 20.3.0
Version: Darwin Kernel Version 20.3.0: Thu Jan 21 00:07:06 PST 2021; root:xnu-7195.81.3~1/RELEASE_X86_64
Machine: x86_64
Processor: i386

#the convert of data informations is based on site: https://www.syngoportal.org/convert

	MMRF_2490_1_BM	MMRF_2499_1_BM	MMRF_1089_1_BM	MMRF_1778_1_BM	MMRF_1777_1_BM	MMRF_1153_1_BM	MMRF_1392_1_BM	MMRF_1325_1_BM	MMRF_1846_1_BM	MMRF_1935_1_BM	MMRF_1654_1_BM	MMRF_1897_1_BM	MMRF_1682_1_BM	MMRF_2339_1_BM	MMRF_2199_1_BM	MMRF_2118_1_BM	MMRF_2487_1_BM	MMRF_2567_1_BM	MMRF_1738_1_BM	MMRF_1695_1_BM	MMRF_2611_1_BM	MMRF_1537_1_BM	MMRF_2267_1_BM	MMRF_1672_1_BM	MMRF_1715_1_BM	MMRF_2404_1_BM	MMRF_1637_1_BM	MMRF_2412_1_BM	MMRF_1640_1_BM	MMRF_1171_1_BM	MMRF_1851_1_PB	MMRF_2378_1_BM	MMRF_2141_1_BM	MMRF_1261_1_BM	MMRF_1648_1_BM	MMRF_2641_1_BM	MMRF_2314_1_BM	MMRF_2646_1_BM	MMRF_2017_1_BM	MMRF_1188_1_BM	MMRF_2456_1_BM	MMRF_2455_1_BM	MMRF_2019_1_BM	MMRF_1344_1_BM	MMRF_1908_1_BM	MMRF_1383_1_BM	MMRF_1112_3_BM	MMRF_1605_1_BM	MMRF_2409_1_BM	MMRF_1867_1_BM	MMRF_1683_1_BM	MMRF_1720_1_BM	MMRF_1730_1_BM	MMRF_1073_1_BM	MMRF_2461_1_BM	MMRF_2330_1_BM	MMRF_1910_1_BM	MMRF_1539_1_BM	MMRF_2055_1_BM	MMRF_1470_1_BM	MMRF_1086_1_BM	MMRF_2201_1_BM	MMRF_1157_4_BM	MMRF_1773_1_BM	MMRF_2539_1_BM	MMRF_1888_1_BM	MMRF_1583_1_BM	MMRF_1474_1_BM	MMRF_1974_1_BM	MMRF_1793_1_BM	MMRF_1232_4_BM	MMRF_1356_1_BM	MMRF_1893_1_BM	MMRF_1269_1_BM	MMRF_2087_2_BM	MMRF_1256_2_BM	MMRF_2522_1_BM	MMRF_2336_1_BM	MMRF_1080_1_BM	MMRF_1242_1_BM	MMRF_1865_1_BM	MMRF_1408_1_BM	MMRF_1296_1_BM	MMRF_1216_1_BM	MMRF_1808_1_BM	MMRF_1266_1_BM	MMRF_2613_1_BM	MMRF_1485_1_BM	MMRF_1922_1_BM	MMRF_2091_1_BM	MMRF_1314_1_BM	MMRF_2601_1_BM	MMRF_1159_1_BM	MMRF_1824_1_BM	MMRF_1698_1_BM	MMRF_2526_1_BM	MMRF_1722_1_BM	MMRF_1859_1_BM	MMRF_1311_1_BM	MMRF_1180_1_BM	MMRF_2002_1_BM	MMRF_1961_1_BM	MMRF_1617_1_BM	MMRF_2062_1_BM	MMRF_1871_1_BM	MMRF_2089_1_BM	MMRF_1499_1_BM	MMRF_1589_1_BM	MMRF_1068_1_BM	MMRF_1831_1_BM	MMRF_1496_3_PB	MMRF_2440_1_BM	MMRF_2353_1_BM	MMRF_1373_1_BM	MMRF_1504_1_BM	MMRF_1842_1_BM	MMRF_1621_1_BM	MMRF_2329_1_BM	MMRF_2458_1_BM	MMRF_1792_1_BM	MMRF_1795_1_BM	MMRF_2216_1_BM	MMRF_1157_3_BM	MMRF_1550_1_BM	MMRF_1511_1_BM	MMRF_2515_1_BM	MMRF_2087_1_BM	MMRF_2026_1_BM	MMRF_1433_1_BM	MMRF_2602_1_BM	MMRF_2538_1_BM	MMRF_2568_1_BM	MMRF_1686_2_BM	MMRF_2400_1_BM	MMRF_2341_1_BM	MMRF_1630_1_BM	MMRF_1327_1_BM	MMRF_2300_1_BM	MMRF_2325_1_BM	MMRF_1952_1_BM	MMRF_1921_1_BM	MMRF_1861_1_BM	MMRF_1596_1_BM	MMRF_2590_1_BM	MMRF_2187_1_PB	MMRF_1169_1_BM	MMRF_2038_1_BM	MMRF_1436_1_BM	MMRF_2211_1_BM	MMRF_2225_1_BM	MMRF_1332_1_BM	MMRF_1061_3_BM	MMRF_1157_1_BM	MMRF_2043_1_BM	MMRF_2379_1_BM	MMRF_1951_1_BM	MMRF_1991_1_BM	MMRF_1900_1_BM	MMRF_2290_1_BM	MMRF_2200_1_BM	MMRF_2119_1_BM	MMRF_1690_1_BM	MMRF_2183_1_BM	MMRF_1130_1_BM	MMRF_1627_1_BM	MMRF_2489_1_BM	MMRF_1284_2_BM	MMRF_2039_1_BM	MMRF_1137_1_BM	MMRF_2050_1_BM	MMRF_1906_1_BM	MMRF_1185_1_BM	MMRF_2453_1_BM	MMRF_2215_1_BM	MMRF_1355_1_BM	MMRF_2438_1_BM	MMRF_1277_1_BM	MMRF_2016_1_BM	MMRF_1790_2_BM	MMRF_1030_1_BM	MMRF_2531_1_PB	MMRF_1971_1_BM	MMRF_2498_1_BM	MMRF_2367_1_BM	MMRF_1365_1_BM	MMRF_1634_1_BM	MMRF_2082_1_BM	MMRF_1506_1_BM	MMRF_1453_1_BM	MMRF_1391_1_BM	MMRF_2548_1_BM	MMRF_1349_1_BM	MMRF_1107_1_BM	MMRF_2429_1_BM	MMRF_1733_1_BM	MMRF_1832_1_BM	MMRF_2642_1_BM	MMRF_1584_1_BM	MMRF_1380_1_BM	MMRF_2331_1_BM	MMRF_1664_1_BM	MMRF_1963_1_BM	MMRF_2523_1_BM	MMRF_1949_1_BM	MMRF_2580_1_BM	MMRF_2608_1_BM	MMRF_1718_1_BM	MMRF_1668_1_BM	MMRF_1426_1_BM	MMRF_1586_1_BM	MMRF_1516_1_BM	MMRF_2465_1_BM	MMRF_1085_3_BM	MMRF_1364_1_BM	MMRF_2017_2_BM	MMRF_2604_1_BM	MMRF_2468_1_BM	MMRF_2291_1_BM	MMRF_1021_1_BM	MMRF_1593_1_BM	MMRF_2328_1_BM	MMRF_2113_1_BM	MMRF_2266_1_BM	MMRF_2599_1_BM	MMRF_1972_1_BM	MMRF_2524_1_BM	MMRF_1786_1_BM	MMRF_1518_3_BM	MMRF_2570_1_BM	MMRF_2069_1_BM	MMRF_1855_1_BM	MMRF_1395_1_BM	MMRF_2195_1_BM	MMRF_1880_1_BM	MMRF_2012_1_BM	MMRF_2231_1_BM	MMRF_2316_1_BM	MMRF_2643_1_BM	MMRF_1490_1_BM	MMRF_1716_1_BM	MMRF_2232_1_BM	MMRF_1401_2_BM	MMRF_1941_1_BM	MMRF_2301_1_BM	MMRF_1881_1_BM	MMRF_2001_1_BM	MMRF_1475_1_BM	MMRF_2080_1_BM	MMRF_1393_1_BM	MMRF_2251_1_BM	MMRF_2057_1_BM	MMRF_1916_1_BM	MMRF_1580_1_BM	MMRF_2445_1_BM	MMRF_2168_1_BM	MMRF_2149_1_BM	MMRF_1977_1_BM	MMRF_1229_1_BM	MMRF_1587_2_BM	MMRF_2305_1_BM	MMRF_1543_1_BM	MMRF_1965_1_BM	MMRF_2490_2_BM	MMRF_2173_1_BM	MMRF_1923_1_BM	MMRF_1152_1_BM	MMRF_1284_1_BM	MMRF_1494_1_BM	MMRF_1389_1_BM	MMRF_2631_1_BM	MMRF_2572_1_BM	MMRF_1281_1_BM	MMRF_1293_1_BM	MMRF_2240_1_BM	MMRF_1830_1_BM	MMRF_2401_2_BM	MMRF_1652_1_BM	MMRF_2250_1_BM	MMRF_2153_1_BM	MMRF_1167_1_BM	MMRF_1401_1_BM	MMRF_1286_1_BM	MMRF_1950_1_BM	MMRF_1766_1_BM	MMRF_1782_1_BM	MMRF_1257_1_BM	MMRF_2151_1_BM	MMRF_1415_1_BM	MMRF_2596_1_BM	MMRF_1854_1_BM	MMRF_1462_1_BM	MMRF_1641_1_BM	MMRF_1371_1_BM	MMRF_2293_1_BM	MMRF_1886_1_BM	MMRF_2297_1_BM	MMRF_2401_1_BM	MMRF_2597_1_BM	MMRF_1424_1_BM	MMRF_1235_2_BM	MMRF_2083_1_BM	MMRF_2335_1_BM	MMRF_2368_1_BM	MMRF_1556_1_BM	MMRF_2541_1_BM	MMRF_2106_1_BM	MMRF_2478_1_BM	MMRF_2005_1_BM	MMRF_1377_1_BM	MMRF_2258_1_BM	MMRF_2457_1_BM	MMRF_1677_1_BM	MMRF_1342_1_BM	MMRF_1295_1_BM	MMRF_1082_1_BM	MMRF_1458_1_BM	MMRF_1454_1_BM	MMRF_1386_1_BM	MMRF_1179_1_BM	MMRF_2514_1_BM	MMRF_2427_1_BM	MMRF_1639_3_BM	MMRF_1538_1_BM	MMRF_1335_1_BM	MMRF_1588_1_BM	MMRF_1891_1_BM	MMRF_2622_1_BM	MMRF_2302_1_BM	MMRF_2056_1_BM	MMRF_1231_1_BM	MMRF_1822_1_BM	MMRF_2317_1_BM	MMRF_1985_1_BM	MMRF_1308_1_BM	MMRF_2485_1_BM	MMRF_2644_1_BM	MMRF_1624_1_BM	MMRF_1432_1_BM	MMRF_1965_1_PB	MMRF_2471_1_BM	MMRF_1024_1_BM	MMRF_1193_3_BM	MMRF_2473_1_BM	MMRF_2543_1_BM	MMRF_1876_1_BM	MMRF_1659_4_BM	MMRF_1656_4_BM	MMRF_2097_1_BM	MMRF_1783_1_BM	MMRF_1510_1_BM	MMRF_1771_1_BM	MMRF_1744_1_BM	MMRF_1714_1_BM	MMRF_2394_1_BM	MMRF_1595_1_BM	MMRF_1360_1_BM	MMRF_1727_1_BM	MMRF_1823_1_BM	MMRF_2587_1_BM	MMRF_1739_1_BM	MMRF_1819_1_BM	MMRF_1603_1_BM	MMRF_2125_1_BM	MMRF_1205_2_BM	MMRF_1157_2_BM	MMRF_1304_1_BM	MMRF_1928_1_BM	MMRF_1860_1_BM	MMRF_1570_1_BM	MMRF_2279_1_BM	MMRF_1312_1_BM	MMRF_1927_1_BM	MMRF_2638_1_BM	MMRF_1348_1_BM	MMRF_2518_1_BM	MMRF_1817_1_BM	MMRF_2380_1_BM	MMRF_1889_2_BM	MMRF_2626_1_BM	MMRF_1029_1_BM	MMRF_1838_3_BM	MMRF_1484_1_BM	MMRF_1573_1_BM	MMRF_1736_1_BM	MMRF_1655_1_PB	MMRF_1323_1_BM	MMRF_2115_1_BM	MMRF_2573_1_BM	MMRF_1513_1_BM	MMRF_2306_1_BM	MMRF_1462_3_BM	MMRF_1352_1_BM	MMRF_2419_2_BM	MMRF_2076_1_BM	MMRF_1290_1_BM	MMRF_2265_1_BM	MMRF_2364_1_BM	MMRF_1250_1_BM	MMRF_1835_1_BM	MMRF_2525_1_BM	MMRF_2452_1_BM	MMRF_1614_1_BM	MMRF_2116_1_BM	MMRF_2107_1_BM	MMRF_1077_1_BM	MMRF_2122_1_BM	MMRF_1522_1_BM	MMRF_2562_1_BM	MMRF_2273_1_BM	MMRF_2384_1_BM	MMRF_1597_1_BM	MMRF_1801_1_BM	MMRF_2203_1_BM	MMRF_1758_1_BM	MMRF_2436_1_BM	MMRF_2606_1_BM	MMRF_1445_1_BM	MMRF_2068_1_BM	MMRF_1151_1_BM	MMRF_1944_1_BM	MMRF_1651_1_BM	MMRF_2422_1_BM	MMRF_2098_1_BM	MMRF_1251_1_BM	MMRF_1889_3_BM	MMRF_1713_1_BM	MMRF_1518_2_BM	MMRF_1455_1_BM	MMRF_2559_1_BM	MMRF_2172_1_BM	MMRF_2081_1_BM	MMRF_1108_1_BM	MMRF_1780_1_BM	MMRF_1512_1_BM	MMRF_1705_1_BM	MMRF_2072_1_BM	MMRF_2635_1_BM	MMRF_1850_1_BM	MMRF_1700_2_BM	MMRF_2560_1_BM	MMRF_2459_1_BM	MMRF_1644_1_BM	MMRF_2194_1_BM	MMRF_2059_1_BM	MMRF_1947_1_BM	MMRF_1533_2_BM	MMRF_1310_1_BM	MMRF_2083_2_BM	MMRF_1992_1_BM	MMRF_1998_1_BM	MMRF_2630_1_BM	MMRF_2614_1_BM	MMRF_1289_1_BM	MMRF_1719_1_BM	MMRF_1224_1_BM	MMRF_2268_1_BM	MMRF_2628_1_BM	MMRF_1967_1_BM	MMRF_1309_1_BM	MMRF_1331_1_BM	MMRF_2475_1_BM	MMRF_1574_1_BM	MMRF_2519_1_BM	MMRF_1572_1_BM	MMRF_1400_1_BM	MMRF_2213_1_BM	MMRF_1407_1_BM	MMRF_1457_1_BM	MMRF_2140_1_BM	MMRF_1078_1_BM	MMRF_2363_2_BM	MMRF_1446_1_BM	MMRF_1978_1_BM	MMRF_1997_1_BM	MMRF_1671_1_BM	MMRF_1491_1_BM	MMRF_1439_1_BM	MMRF_2111_1_BM	MMRF_1049_2_BM	MMRF_1307_1_BM	MMRF_2480_1_BM	MMRF_2362_1_BM	MMRF_1755_1_BM	MMRF_1726_1_BM	MMRF_2326_1_BM	MMRF_1346_1_BM	MMRF_1359_1_BM	MMRF_1193_5_BM	MMRF_2229_1_BM	MMRF_1686_1_BM	MMRF_1721_1_BM	MMRF_1235_1_BM	MMRF_1650_2_BM	MMRF_1814_1_BM	MMRF_2197_1_BM	MMRF_2041_1_BM	MMRF_1731_1_BM	MMRF_2093_1_BM	MMRF_1502_1_BM	MMRF_2605_1_BM	MMRF_1579_1_BM	MMRF_1374_1_BM	MMRF_1768_1_BM	MMRF_2238_1_BM	MMRF_2307_1_BM	MMRF_1210_1_BM	MMRF_2089_2_BM	MMRF_1388_1_BM	MMRF_2550_1_BM	MMRF_1037_1_BM	MMRF_1833_1_BM	MMRF_2035_1_BM	MMRF_1260_1_BM	MMRF_1659_1_BM	MMRF_1534_1_BM	MMRF_2532_1_BM	MMRF_1100_1_BM	MMRF_1735_1_BM	MMRF_1546_1_BM	MMRF_1665_1_BM	MMRF_2634_1_BM	MMRF_1252_1_BM	MMRF_1678_1_BM	MMRF_2433_1_BM	MMRF_2554_1_BM	MMRF_1810_1_BM	MMRF_1882_1_BM	MMRF_1927_1_PB	MMRF_2595_1_BM	MMRF_1839_1_BM	MMRF_1137_3_BM	MMRF_2074_1_BM	MMRF_2497_1_BM	MMRF_2245_1_BM	MMRF_2449_1_BM	MMRF_1358_1_BM	MMRF_1164_1_BM	MMRF_1496_1_PB	MMRF_1148_1_BM	MMRF_1587_1_BM	MMRF_2352_1_BM	MMRF_1024_2_BM	MMRF_2039_2_BM	MMRF_1178_1_BM	MMRF_1098_1_BM	MMRF_1219_2_BM	MMRF_1807_1_BM	MMRF_1092_1_BM	MMRF_1982_1_BM	MMRF_2531_1_BM	MMRF_2269_1_BM	MMRF_1613_1_BM	MMRF_1032_1_BM	MMRF_1560_1_BM	MMRF_1992_2_BM	MMRF_1223_2_BM	MMRF_2407_1_BM	MMRF_2054_1_BM	MMRF_2204_1_BM	MMRF_2377_1_BM	MMRF_1936_1_BM	MMRF_1403_1_BM	MMRF_1450_1_BM	MMRF_2126_1_BM	MMRF_2061_1_BM	MMRF_1625_1_BM	MMRF_2176_1_BM	MMRF_1618_1_BM	MMRF_1501_1_BM	MMRF_2469_1_BM	MMRF_1796_1_BM	MMRF_1031_1_BM	MMRF_1413_1_BM	MMRF_1750_1_BM	MMRF_2271_1_BM	MMRF_2636_1_BM	MMRF_2018_1_BM	MMRF_2564_1_BM	MMRF_2581_1_BM	MMRF_2516_1_BM	MMRF_2444_1_BM	MMRF_1520_1_BM	MMRF_1045_1_BM	MMRF_1565_1_BM	MMRF_1981_1_BM	MMRF_2015_1_BM	MMRF_2574_1_BM	MMRF_1790_1_BM	MMRF_1541_1_BM	MMRF_1627_3_BM	MMRF_1837_1_BM	MMRF_1244_1_BM	MMRF_1856_1_BM	MMRF_1862_1_BM	MMRF_1434_1_BM	MMRF_1851_1_BM	MMRF_1320_1_BM	MMRF_1785_1_BM	MMRF_2579_1_BM	MMRF_1523_1_BM	MMRF_2180_1_BM	MMRF_1879_1_BM	MMRF_1274_1_BM	MMRF_1099_1_BM	MMRF_2621_1_PB	MMRF_1184_1_BM	MMRF_2313_1_BM	MMRF_1128_1_BM	MMRF_2476_1_BM	MMRF_1932_1_BM	MMRF_1825_1_BM	MMRF_1895_1_BM	MMRF_1131_2_BM	MMRF_1195_1_BM	MMRF_1897_1_PB	MMRF_2228_1_BM	MMRF_1201_1_BM	MMRF_2286_1_BM	MMRF_1211_1_BM	MMRF_1845_1_BM	MMRF_1542_1_BM	MMRF_2535_1_BM	MMRF_1815_1_BM	MMRF_1033_1_BM	MMRF_1496_1_BM	MMRF_1361_1_BM	MMRF_1710_1_BM	MMRF_2501_1_BM	MMRF_1129_1_BM	MMRF_1461_1_BM	MMRF_1890_1_BM	MMRF_2373_1_BM	MMRF_1917_1_BM	MMRF_1911_1_BM	MMRF_1643_1_BM	MMRF_1639_1_BM	MMRF_1447_1_BM	MMRF_1460_1_BM	MMRF_1229_2_BM	MMRF_2047_1_BM	MMRF_1423_1_BM	MMRF_1038_1_BM	MMRF_2365_1_BM	MMRF_1202_1_BM	MMRF_1466_1_BM	MMRF_1519_1_BM	MMRF_2598_1_BM	MMRF_2637_1_BM	MMRF_1338_1_BM	MMRF_1318_1_BM	MMRF_2013_1_BM	MMRF_1540_1_BM	MMRF_1915_1_BM	MMRF_2310_1_BM	MMRF_1452_1_BM	MMRF_1179_2_BM	MMRF_1577_1_BM	MMRF_2366_1_BM	MMRF_1489_1_BM	MMRF_1212_1_BM	MMRF_1633_1_BM	MMRF_2621_1_BM	MMRF_1137_4_BM	MMRF_2079_1_BM	MMRF_1778_1_PB	MMRF_2224_1_BM	MMRF_1324_1_BM	MMRF_1759_1_BM	MMRF_2506_1_BM	MMRF_2006_1_BM	MMRF_2170_1_BM	MMRF_1496_3_BM	MMRF_2464_1_BM	MMRF_2102_1_BM	MMRF_2419_1_BM	MMRF_1285_1_BM	MMRF_1354_1_BM	MMRF_1857_1_BM	MMRF_1689_1_BM	MMRF_2174_1_BM	MMRF_1783_2_BM	MMRF_1802_1_BM	MMRF_2236_1_BM	MMRF_2187_1_BM	MMRF_1189_1_BM	MMRF_1645_1_BM	MMRF_1024_3_BM	MMRF_1992_1_PB	MMRF_2549_1_BM	MMRF_1931_1_BM	MMRF_1270_1_BM	MMRF_2217_1_BM	MMRF_2399_1_BM	MMRF_1267_1_BM	MMRF_2257_1_BM	MMRF_2505_1_BM	MMRF_1229_3_BM	MMRF_2639_1_BM	MMRF_1797_1_BM	MMRF_1309_2_BM	MMRF_1285_3_BM	MMRF_1598_1_BM	MMRF_2214_1_BM	MMRF_2230_1_BM	MMRF_2413_1_BM	MMRF_2000_1_BM	MMRF_1328_1_BM	MMRF_1451_1_BM	MMRF_1531_1_BM	MMRF_1478_1_BM	MMRF_2253_1_BM	MMRF_1775_1_BM	MMRF_2150_1_BM	MMRF_2488_1_BM	MMRF_1656_1_BM	MMRF_2583_1_BM	MMRF_2272_1_BM	MMRF_2557_1_BM	MMRF_1300_1_BM	MMRF_1425_1_BM	MMRF_2220_1_BM	MMRF_1694_1_BM	MMRF_2143_1_BM	MMRF_1912_1_PB	MMRF_1847_1_BM	MMRF_1787_1_BM	MMRF_1048_1_BM	MMRF_2437_1_BM	MMRF_1242_5_BM	MMRF_1497_1_BM	MMRF_1380_2_BM	MMRF_1700_1_BM	MMRF_1918_1_BM	MMRF_1670_1_BM
1	18.391400	15.189000	3.032370	0.584036	25.451500	5.321400	30.233400	20.331900	6.974960	15.087800	13.499600	4.011460	23.798700	25.989400	0.660751	41.899300	19.078800	45.064500	0.264560	2.244930	24.194000	47.283900	0.593017	56.077300	0.098929	4.391990	3.205900	36.669000	0.109408	15.011300	12.642900	12.462200	11.067900	37.186400	0.120342	32.879300	0.049342	31.402000	28.473100	18.432800	0.676623	25.048700	10.651400	44.765500	9.445470	0.041544	19.844000	1.760550	25.937400	59.129300	0.117246	0.670258	0.561156	35.957300	2.039290	34.257700	23.147300	13.293200	30.255500	1.677350	65.765100	20.784100	30.076700	33.521900	21.681800	0.132867	50.384900	31.698900	8.997670	0.795994	1.336020	32.993700	1.676660	26.825000	6.330970	2.190660	45.388500	0.161413	7.861250	0.378535	1.430950	24.402000	22.053300	9.930520	14.263300	21.452500	5.434100	3.793440	22.387300	7.376490	31.808000	0.662124	0.062073	6.392540	18.892800	4.445370	55.873600	19.874200	20.802300	0.057677	0.148720	21.768600	18.537800	16.886300	33.712500	1.189700	15.106700	1.606080	28.033500	31.040800	0.058064	1.513570	24.990600	23.148700	19.268000	12.737000	19.310700	36.385000	6.478130	0.088852	50.417000	34.276200	20.026200	11.043100	14.205000	0.647872	1.576410	0.142328	0.044314	1.428410	24.155400	30.727600	27.408500	10.526000	38.485600	0.808408	0.299047	10.937400	29.568900	5.728800	14.095000	5.244050	2.367680	26.869200	0.161615	23.828400	1.530290	16.154300	11.017300	0.685519	27.066300	15.529000	8.610060	0.304834	55.504500	0.056164	0.098744	15.253400	17.211100	12.187800	33.954500	43.619000	11.384700	2.696830	0.512312	0.411547	11.031600	0.092622	13.956400	3.678300	25.777500	27.254500	2.631960	0.800415	1.108510	17.175800	14.884800	16.315400	18.276300	19.161100	9.249780	0.327154	12.847900	33.460800	2.041370	9.154950	25.322600	1.661800	0.105586	0.587597	0.144509	0.047573	10.078000	15.625300	14.957600	0.758047	0.406374	14.175000	45.994900	24.122000	17.061500	37.131300	0.349325	33.580500	1.222640	41.778800	10.328600	0.185692	9.123750	0.064230	13.295400	23.547400	21.640200	0.417667	14.192600	21.494600	2.864150	2.342220	1.911520	44.586700	69.656400	1.174950	15.036000	1.705150	29.700800	13.166000	0.823351	0.101977	33.226900	4.006920	27.254700	0.283098	14.956200	1.455540	17.047300	0.781321	29.199600	14.339000	5.399530	41.465800	3.223280	13.300200	5.080540	23.653800	22.973400	1.130570	0.123740	31.496900	34.146400	17.414700	0.740960	11.017200	7.973520	16.323300	5.677190	4.567340	36.704200	27.480100	39.201900	27.095100	41.760300	0.645652	37.848500	21.114500	0.412090	2.218400	11.328200	3.367450	7.692930	64.232000	40.095100	35.884100	1.331640	5.184080	1.343000	10.268100	22.178800	18.694600	16.711000	26.147500	9.030260	30.635600	16.433300	60.912200	19.593300	6.353790	30.059500	22.339700	0.891303	0.319658	17.420600	6.755010	19.386600	0.228087	21.421300	35.135800	1.459320	21.652100	29.422500	17.073700	18.770800	20.620600	5.669390	20.921600	0.043256	0.034281	17.883600	21.133400	45.172100	23.713600	0.532119	0.180387	15.863700	20.751100	25.894900	26.953500	5.537100	21.340300	6.831220	25.951000	0.300116	15.462200	9.222020	0.855502	22.489600	0.071925	6.396070	1.871830	15.382700	12.533200	2.635730	7.545980	23.204000	27.868800	22.492100	4.991910	14.406100	28.928500	0.315627	0.281028	0.261432	3.999350	0.215079	23.359100	0.409624	1.907630	3.941460	38.159600	30.672800	4.351640	23.756600	21.294900	28.287500	0.061597	11.358600	18.385800	12.265200	23.218400	1.456780	0.079813	14.976200	1.714800	3.187670	8.259510	18.281700	27.447100	0.107477	19.205200	11.071000	0.900060	4.193780	1.244390	32.031900	21.413300	19.261500	35.758300	0.242333	11.377900	13.280300	19.098200	1.737230	17.014500	28.210100	15.793200	0.000000	11.268000	0.277421	15.831200	0.356247	9.522950	37.172900	8.316680	0.925815	0.153496	0.041505	18.266200	42.663000	3.730580	13.840000	1.743370	20.004500	4.345980	27.816000	43.170000	14.229000	1.145880	8.209780	6.491950	19.255700	28.500400	5.127450	0.166815	21.406900	60.133700	47.068100	12.502000	32.188300	1.133940	16.584500	33.053400	21.774300	1.911300	5.505930	0.000000	10.041800	0.125011	0.009897	17.066000	28.940400	15.855000	3.417540	0.477047	27.784100	0.038433	72.189500	22.624500	16.901900	9.718620	20.214800	17.836700	19.309100	0.874191	45.806600	29.983600	0.071088	36.920400	2.311820	11.895500	0.500027	1.604080	19.876700	24.939700	4.450660	2.143360	23.882000	13.415400	62.889200	6.939820	0.153227	37.200300	11.734700	20.107400	37.524500	23.415600	106.111000	0.072334	36.039300	17.742400	17.379000	0.350184	10.575900	16.982300	0.854393	3.483150	0.584160	0.263696	32.878900	1.694470	44.286700	0.381292	14.321200	3.516000	7.007990	10.698000	0.468812	1.246330	17.790900	2.068330	29.864100	27.047700	16.874900	0.157622	16.858300	10.509300	20.125100	0.296187	19.203400	25.978900	6.730540	21.190500	2.947680	13.080600	27.264300	0.078162	35.670900	14.646100	1.478740	9.276580	6.401830	12.910600	0.106269	9.533770	17.226200	0.154387	1.592490	22.307300	17.263000	12.453100	28.059800	23.681800	13.129100	9.672310	0.932410	10.151400	54.585300	0.244049	8.169400	0.603064	20.672800	2.336360	6.441010	4.428720	0.194957	33.134400	30.580300	47.957100	6.727280	0.139276	0.364290	58.673700	57.882400	0.123506	0.408727	9.029960	38.315900	5.243870	33.837300	10.652500	20.744200	9.324780	0.039250	20.042800	8.240950	16.735200	2.657320	34.254900	36.558400	29.505200	13.731600	34.438100	3.806590	0.000000	2.531650	30.904200	37.145200	8.786810	19.372200	0.030993	4.002170	19.961300	0.067114	8.469910	60.396700	10.293500	0.538978	18.682600	20.134400	42.430000	14.181300	1.479510	85.580300	7.161280	5.350370	30.543900	21.804900	0.448467	21.033200	16.168200	8.151290	0.260408	3.442060	15.537500	17.035100	1.870370	29.221300	10.571300	1.938020	11.389400	29.160700	0.125876	24.335100	16.436800	31.631000	4.945210	2.271090	3.216250	4.602890	0.053858	11.488000	0.335201	16.794800	3.065120	0.076177	0.000000	1.519020	48.648600	28.078000	1.845450	28.751600	34.389000	5.181710	9.655510	0.045995	15.047400	0.007955	21.439700	23.761200	0.210882	8.595900	8.938050	6.984940	0.048586	5.110350	37.155100	2.623050	21.433500	28.120200	5.802510	34.000900	49.962700	34.426700	17.934400	3.409100	0.068274	13.825700	8.746390	0.115903	0.989168	6.919340	1.611990	31.910000	15.325900	45.105700	14.634400	6.879920	29.417700	6.901960	27.040000	14.854800	20.969700	2.741800	3.586660	1.957700	22.691800	0.000000	0.361487	14.724800	3.196420	0.048461	15.854200	49.956900	0.121350	13.216000	18.015700	1.675200	2.288100	4.218470	8.786140	51.348200	0.075495	21.501700	7.357780	41.913500	0.007046	13.257200	14.160700	0.059267	0.443687	21.233600	9.283030	10.899400	52.103200	14.706000	11.137800	2.428210	10.510000	36.915700	10.655600	2.078920	0.416375	4.947960	35.592700	0.142215	11.577700	0.506111	21.718200	0.578136	50.226900	1.955730	0.009741	9.104770	28.868700	0.290643	24.903800	3.098000	0.767144	0.000000	27.674100	0.073463	43.576900	27.310900	18.366800	19.503000	9.594920	0.161310	26.763700	2.778470	7.703320	1.049320	17.505600	40.979600	34.538400	3.786580	7.078480
2	0.086096	13.214000	1.802760	0.052207	7.977120	0.872957	0.673016	0.976726	0.371472	0.636931	0.566991	0.111364	1.133220	4.704100	8.967020	5.556800	1.874950	1.265240	0.834619	3.627040	5.287450	5.769560	3.239040	1.204450	0.396376	1.767040	0.287335	1.475900	1.091920	1.471460	0.030863	0.365453	5.573510	0.128741	0.094010	0.597354	1.228400	1.439540	0.960562	2.562640	0.469854	0.164598	10.919400	0.320393	0.318935	3.339260	13.490300	2.362560	0.495744	0.214143	1.786860	0.080377	1.260000	0.187857	14.965100	0.379369	2.025010	0.091849	0.742152	8.285640	0.817617	30.637100	0.203206	0.797991	1.067950	1.562780	0.374503	0.111611	3.697480	0.204679	1.890210	0.210499	42.626600	0.703114	15.803800	3.880480	0.246182	1.006990	1.557760	2.860380	0.089468	2.296920	0.972775	0.269064	15.743300	0.421393	0.557925	3.597000	1.539250	0.164937	0.541907	1.385270	13.171100	30.745300	0.114997	0.683161	0.675425	6.573700	0.126543	0.135415	1.975990	0.255665	2.202020	1.693910	0.189223	0.306879	6.174920	1.371150	0.693572	12.162500	0.003786	0.423403	0.256035	1.390330	0.376787	0.287684	3.450780	0.894437	0.883476	0.315868	1.446080	2.670380	0.123314	0.069241	0.299502	1.123230	2.706190	0.070131	0.869276	1.890930	3.076860	2.110330	0.155843	0.532828	2.185910	2.410450	0.339285	0.475378	0.353483	1.813380	4.148870	0.771601	0.423925	0.851348	0.513808	15.962900	0.922871	2.098930	1.657290	5.290760	1.233430	35.836100	0.731105	0.197308	0.891544	0.756822	0.254444	1.430910	0.313425	0.099019	0.379083	1.507220	0.531044	1.321830	0.145857	3.541830	54.948900	0.388895	4.013310	0.092043	0.463211	5.777940	15.225100	3.824430	2.479780	11.306300	0.067294	0.220445	3.061500	3.666870	0.358822	0.167842	0.080519	2.581790	0.698492	0.965013	0.121400	0.577859	1.696910	0.720726	2.943400	0.084679	0.257250	1.124380	21.328900	0.176072	0.355952	1.187090	1.471430	0.816382	2.853570	2.728170	0.073394	0.201123	1.358540	3.606580	34.001500	2.979860	1.061060	0.086654	2.389750	1.738420	2.317180	1.880320	7.219600	0.234958	2.024220	19.971800	1.052220	1.338450	1.127380	1.869430	0.168444	5.811340	1.685540	1.851280	0.100683	1.673870	0.591600	3.113160	0.341812	5.562380	0.706672	0.724820	2.888050	0.309996	0.867799	0.282184	1.107500	3.261110	1.660530	1.802290	6.733160	5.431970	0.370832	0.219800	0.233751	0.299044	2.837270	0.269525	0.206811	0.807424	0.638430	36.466100	2.081440	2.410720	2.496040	0.114179	2.034540	13.820900	0.037089	0.186480	1.383260	0.346069	0.868912	0.388468	18.650500	3.882910	0.163390	0.947428	0.066630	3.708550	0.428250	1.330290	0.253100	0.806196	0.197933	0.464564	31.560400	1.800710	1.278550	0.451265	0.162282	0.184327	3.114140	0.250206	0.749812	0.418820	0.828225	1.389770	6.951420	1.138230	1.030320	4.964420	2.917360	3.267640	1.573180	0.627583	0.298043	79.240400	0.595288	3.269920	0.620375	2.378660	0.007856	0.089314	5.885150	0.947688	0.720432	19.407600	0.236967	0.241704	0.340970	1.085210	0.453359	0.963572	0.319986	0.246772	0.384173	1.597290	0.547785	1.149310	0.373228	0.144076	2.379610	0.145593	1.168200	3.567140	7.991160	5.905460	0.047869	0.284031	6.727970	42.054800	0.586082	2.973010	0.100077	0.359504	0.054991	9.994040	2.536600	2.891440	0.759188	0.648511	0.366381	37.069200	6.347210	1.852930	1.473820	0.344677	0.568569	2.288450	1.216930	0.867241	1.915270	2.592640	1.597870	0.757984	0.312316	0.357749	0.204491	1.517930	0.256465	11.477800	0.748093	0.929316	0.996123	7.539130	4.120280	3.474150	0.596727	3.963860	0.655582	0.206058	0.274570	3.364090	0.543241	0.600157	1.722620	2.618610	8.816720	0.295117	0.944224	0.481129	0.084306	5.814790	0.519743	0.819209	3.592640	0.125144	3.745700	1.255960	0.503552	0.027257	0.182880	3.547220	0.725361	7.316460	0.408419	0.274483	1.320500	0.049605	4.831850	9.566070	0.981416	1.001220	3.980930	2.287260	3.872550	2.081570	1.184160	0.297715	0.295790	1.225680	3.023140	1.347490	0.134311	2.616150	1.784430	6.424080	0.332095	1.310420	3.404240	0.836400	0.885644	2.543080	0.148352	3.343800	1.269390	0.419039	0.252439	1.063020	0.539946	0.089980	0.628295	1.345650	3.259240	0.912074	20.894200	1.845570	1.892570	0.141789	0.388019	6.135690	0.622808	6.120160	2.471300	0.694530	0.659343	2.728960	0.785683	21.683300	1.646720	6.240320	0.193384	2.950150	0.081017	1.718780	0.701084	1.727220	0.873983	0.429383	0.401064	3.987570	0.618035	0.594006	70.797600	0.650058	0.364814	0.118504	1.064380	0.111957	1.225450	16.729000	0.723812	5.709310	0.463457	0.622122	7.406090	17.629900	4.442860	6.934810	5.199770	5.379030	2.103240	1.951140	0.926841	0.694925	0.407473	0.287940	4.099900	2.287780	15.122200	6.042650	2.113550	1.173340	0.517750	1.654690	0.901243	0.494148	2.006700	1.072230	0.309413	0.508025	2.118710	0.821226	0.229364	0.419135	0.202718	3.062260	0.275583	4.391080	14.519100	0.335742	3.718550	1.741550	0.092327	4.745220	0.334428	2.248720	0.171361	3.627660	0.512160	6.963670	0.418523	0.140912	1.605460	0.000000	1.000000	1.144610	34.452200	0.855565	0.103096	0.606517	6.922040	0.253345	6.384480	0.049289	0.782945	1.798830	1.316970	0.944862	3.674580	0.216533	0.049407	7.977380	10.798200	0.188348	0.389800	0.424444	0.039788	2.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9692	11.472770	1.150095	1.999532	2.330912	1.850304	4.621378	1.859862	1.682133	1.862996	1.418126	3.648833	3.323078	3.271476	3.265198	1.323357	2.598873	2.054016	1.174649	3.963626	2.222999	3.612909	1.347267	3.021116	0.783225	6.193117	5.745559	6.224507	3.392212	1.601595	3.321071	4.327825	3.750830	1.711909	3.250439	1.666867	1.419936	6.393033	1.522533	0.933468	2.152980	3.669679	8.553858	1.413447	4.897994	2.711302	1.992006	1.438754	1.491328	1.815222	2.260921	3.052510	3.326716	1.140011	1.871043	1.329393	1.911969	0.950304	3.136149	2.351230	3.117439	1.149860	3.538075	1.720676	2.489030	0.749747	3.874383	2.905009	3.666562	3.125901	0.810554	5.476112	3.186960	2.405359	3.897288	5.375428	1.964763	4.712328	2.175963	1.322204	6.098300	1.300062	2.140662	6.573835	1.444803	0.993398	2.766281	3.438017	2.152767	3.426452	4.277401	1.075149	2.828772	5.259299	1.619703	1.247156	2.023550	5.298381	2.293283	1.807429	4.414511	2.417925	2.906227	3.425251	3.753394	1.605061	3.319026	4.375900	2.457224	2.928179	3.219156	5.406834	2.275289	1.903004	2.286529	3.522797	2.815726	1.094326	2.470353	2.544557	2.577878	2.728413	1.541947	2.182571	6.268688	2.622247	7.268843	2.522984	0.828991	1.547305	1.491292	4.507601	9.990310	3.064025	3.221934	3.598028	1.999923	1.440864	11.992545	1.985968	1.913195	1.583830	1.233440	2.853411	4.918824	7.397567	4.261991	1.714030	0.810987	4.960197	9.312010	2.441707	1.090820	3.185043	2.719215	4.231592	7.353593	2.570511	1.803530	5.165222	3.891625	4.754682	1.154662	2.467196	1.464200	2.074955	2.441568	2.088676	4.506089	2.067766	0.366765	2.674733	2.282113	2.966051	2.008871	1.980450	4.517470	5.536037	1.215824	2.289996	1.633171	7.788870	3.251553	2.342288	3.002047	2.719172	3.088927	1.438158	6.365430	2.675791	1.170735	1.949077	1.296725	3.404015	2.023518	1.165961	13.745093	2.294756	1.718187	1.820178	1.820364	1.931284	1.611905	3.208620	5.041203	4.378927	3.977473	1.999160	2.786901	2.206283	3.989454	1.384278	3.334742	2.957238	5.407434	2.156847	1.900883	2.454298	1.855922	1.880706	2.233296	3.639358	1.644931	3.799648	2.484455	1.966241	2.768153	1.541255	20.081045	1.138240	3.437075	4.506482	2.173059	1.757306	1.294822	6.163562	4.063264	1.323833	1.456341	2.981402	1.823301	2.803196	4.102618	1.031220	2.391860	1.382272	3.702212	3.938725	2.081685	0.884148	4.179006	3.583319	5.051045	4.423741	2.774140	2.237482	4.173645	2.797538	4.445862	5.867471	3.782621	0.980855	5.337293	3.778961	3.804589	8.737994	3.174946	0.764389	4.035137	6.400771	6.872518	2.965273	2.278125	2.447754	2.256715	3.454150	2.126457	3.074909	2.053068	2.338873	6.224886	3.327145	3.855608	5.545045	4.550815	1.674675	4.239291	1.721998	7.329495	1.961316	3.733214	1.576974	3.010895	2.572115	4.698693	3.280813	1.236536	4.749465	5.696846	4.538675	2.170610	2.789370	1.941718	1.756902	2.100487	5.893176	7.716580	1.459650	2.165296	5.714061	2.559485	5.546175	1.321315	2.682549	1.435035	16.416885	2.300310	3.081299	2.954544	1.934179	4.628813	12.664930	5.238925	2.262181	1.592807	1.418592	2.434277	1.839560	1.995696	2.602920	1.338383	2.600612	1.371347	4.927237	3.650850	5.747706	2.534050	11.599520	2.124922	7.366079	3.440046	2.262034	7.512306	2.208157	0.826221	3.308975	2.358630	8.427970	1.883154	1.234181	6.046148	5.550474	2.650266	1.767123	3.200442	3.656366	2.093305	3.894063	1.009327	4.092809	3.668274	1.400237	2.496738	3.195165	1.871027	2.977910	2.254106	5.792567	1.631423	1.294548	2.359167	2.096499	2.487818	0.845832	1.283205	1.370144	2.512548	3.046272	3.361493	0.828291	3.503978	1.169423	2.392591	1.049832	4.263114	4.158748	4.659840	3.626375	0.616440	1.229413	2.404172	3.473344	0.876395	2.821487	4.309151	2.748011	2.567381	2.188847	3.786104	2.217995	2.253630	0.729344	2.013492	1.689096	3.709774	1.871396	3.122645	2.476882	4.491953	2.956127	1.648424	4.079770	7.059945	1.639972	3.834246	2.887272	1.369706	1.687323	3.754929	4.840622	1.734894	2.422468	2.480056	1.414686	1.822599	4.538000	4.995730	22.903580	0.756486	1.045012	0.694132	5.315538	2.549073	7.170500	1.822677	1.017986	3.675750	1.759295	3.763400	3.898476	1.843824	1.610577	5.043455	8.156939	5.303593	2.977851	7.328450	4.048279	3.275026	3.398848	2.326558	3.016430	2.190768	2.849563	4.133268	2.009041	2.121005	0.843714	3.089675	1.327797	2.196290	1.540361	1.941058	1.020460	3.004057	1.703390	2.937015	3.939473	1.631020	2.136344	3.332495	1.372791	1.950199	1.130842	1.880114	3.798569	3.784839	4.360651	2.401221	2.256509	8.321955	2.492837	2.718937	6.536407	3.688274	0.474810	1.445850	3.354055	4.781642	1.165515	1.968302	1.335681	4.272055	1.920698	4.701732	1.997387	9.165895	2.656705	2.233097	2.645430	3.375656	3.627426	1.750619	4.591336	2.490949	3.734145	2.481707	3.773281	4.022754	2.056426	1.339019	1.121610	4.065100	0.919140	3.651433	2.710204	3.572343	1.496270	1.737391	4.294099	2.447271	1.228350	1.101902	2.292248	3.159549	4.914396	4.102423	1.525012	3.480497	3.640793	3.515106	2.477540	2.493612	1.901206	4.009380	2.184220	1.341662	6.157101	2.585357	3.196833	1.639307	1.603826	1.623860	5.936431	2.530812	1.763738	2.056455	1.325928	1.381793	2.461551	6.736148	3.550673	1.695860	2.420710	2.023481	2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9720	1.256925	1.169509	1.350701	0.374735	2.086949	7.011621	48.960855	8.738658	0.251819	1.007040	12.820465	1.641911	2.916194	3.245022	0.993925	9.452490	5.595343	0.654690	0.375735	9.440273	0.517309	1.520402	0.456220	1.084116	2.485640	1.938618	9.760419	6.574650	0.756380	12.214403	29.648935	4.658257	1.225754	0.952044	1.693277	0.595096	1.039760	0.267115	0.432406	0.898827	8.658406	1.344211	4.675999	0.952220	9.954905	2.899945	1.105309	30.755935	15.824364	8.574338	0.337526	1.634642	1.567255	3.132770	2.554828	1.660993	6.631790	2.463859	4.339947	4.359703	2.987879	13.241495	0.071740	0.342121	3.032797	5.911137	8.604064	2.476689	5.195093	7.433551	1.672140	2.521335	0.903510	0.244397	0.511268	0.368266	3.540926	0.336979	0.914562	0.744189	1.179002	3.155683	2.481760	8.933840	6.361457	34.267345	0.416640	6.070820	0.239345	2.745794	1.024296	1.197190	48.740050	0.792576	2.103762	1.910088	4.458312	6.977409	28.257140	0.117274	3.197320	0.546530	6.383893	2.536718	1.401930	0.273322	7.943700	1.101900	0.368402	6.213242	2.174351	3.273500	0.354660	3.080181	1.795987	0.381365	1.642176	1.410021	0.583954	2.651463	0.057576	2.929653	0.220263	5.309627	2.467395	7.399237	1.209854	1.386540	13.108315	3.432039	8.179098	3.919220	2.295782	1.921343	6.537244	2.298927	34.529565	0.315929	3.029124	2.595578	1.583419	4.322412	8.821527	1.979073	1.484457	1.157545	8.978837	3.312273	1.439811	2.676706	5.736948	0.749423	2.254148	8.463795	2.010578	0.607781	11.702838	3.043431	0.754841	1.884313	3.319548	0.993626	0.364914	3.790160	10.038199	6.997724	4.003329	1.293894	10.830412	0.477885	4.130505	0.833404	0.597743	0.803898	1.627731	2.473537	3.807962	0.083488	6.562668	0.310501	7.626430	0.496540	4.733353	0.132883	1.329660	4.406754	2.108740	9.757530	4.689914	9.850920	0.994557	6.747393	36.489235	1.683817	2.089974	2.488572	0.683210	1.131697	0.289362	0.229124	3.902058	6.459192	2.491941	1.216233	2.943935	0.023733	1.330141	18.680228	3.506826	4.054616	0.938755	1.013884	1.315746	2.106166	1.275421	1.324626	5.505193	0.103587	0.821162	8.315283	0.759038	3.825605	11.122275	1.462682	0.670043	18.979758	0.803940	2.273278	2.104335	2.360600	3.325959	0.474375	1.512406	0.225632	0.207411	0.282699	0.914989	4.672376	3.414324	0.830461	0.173293	0.236318	12.519917	1.731490	0.597906	2.015603	7.220280	4.487557	2.209354	7.842162	1.202381	0.839560	4.628034	3.721140	1.438065	8.710680	26.570505	10.631251	0.377693	0.809286	0.357321	0.634332	0.131413	0.655311	0.741661	1.867470	3.991341	14.608213	4.501863	0.222344	1.788350	3.498254	1.072454	1.741148	0.795799	0.693482	1.722497	3.622169	4.593953	0.841907	0.542971	11.634085	4.145736	5.718070	1.088823	3.149573	1.025305	0.596814	10.729138	1.044829	7.667597	0.010768	0.212297	0.363664	11.191999	0.548024	3.755452	3.475605	3.888922	0.561533	1.966431	1.892143	2.727809	1.832851	0.140187	0.376959	0.389182	0.200820	10.830918	0.270412	38.055475	2.112549	6.230812	1.380548	0.056959	14.092481	1.856135	0.835291	1.102576	2.755450	8.439313	0.921688	1.819479	8.971469	0.948496	1.620029	0.031924	7.248568	0.401579	0.291375	0.819081	10.089816	1.941716	2.798746	0.533860	0.646653	3.163989	0.613272	0.652305	14.843077	0.441802	7.266050	7.102856	0.683078	0.045594	0.042263	7.530016	0.665227	1.079547	0.951110	2.655524	11.418243	5.867057	0.493827	3.465025	4.291378	5.817893	1.760325	6.574255	1.182591	0.383681	4.680644	0.957310	0.432122	0.333906	3.132649	5.806243	3.098465	0.412639	11.953762	1.484648	6.429925	1.251694	4.886840	1.799254	0.649558	1.361329	2.077402	1.713620	1.361006	1.267400	43.087830	17.882373	5.238498	5.009642	2.531222	1.232860	4.523739	16.478141	0.091046	5.752816	3.616036	0.292640	3.208988	2.081722	6.455603	1.997286	2.897579	13.064706	1.938316	0.326411	0.571500	5.557078	4.011639	0.441048	16.586905	1.890776	4.216044	0.609412	6.823637	2.052519	0.386459	0.566338	4.818447	1.344123	2.066629	10.903764	1.756620	7.325044	0.374211	12.992231	22.579445	2.853299	1.053155	0.325597	1.661102	0.819968	11.841814	8.373057	1.017968	4.029302	9.791467	12.070486	2.417135	0.124339	0.496528	0.074530	2.873994	1.450239	0.800681	0.453547	1.676797	2.835338	3.233056	9.790018	0.671702	0.026683	4.577420	0.217071	14.441790	1.011511	3.826602	9.374242	10.969392	18.285395	0.130842	0.215534	1.597378	12.515185	3.858049	4.541945	2.635182	0.178014	1.461099	0.103685	0.291843	1.043523	0.572853	0.005655	0.136724	0.003832	1.780828	2.602422	6.754226	9.122597	1.689990	3.073422	0.984488	1.941740	0.674390	0.526326	8.788294	2.494760	11.101023	2.369686	1.020480	1.414170	3.040491	0.893594	0.671140	2.438779	85.381410	0.014336	2.309714	1.768647	0.799930	7.331685	2.823794	7.028582	22.126535	5.640185	1.455382	6.304538	1.149477	1.215580	2.875413	0.031108	15.478195	3.629143	0.433801	3.294490	3.247522	11.721383	0.925830	4.998906	8.644677	0.383878	2.106555	5.925400	5.810122	11.627145	11.597936	1.545318	0.632825	1.913760	7.318738	4.524891	3.611727	1.704595	3.237737	4.712040	2.969358	1.390642	1.591522	12.241730	1.671609	0.599721	0.834009	1.476356	5.830900	7.681739	18.879080	7.802441	14.504188	15.148060	7.229148	0.900250	1.240943	4.225352	4.941037	1.252215	10.954990	0.352493	1.075390	0.006802	0.376219	2.970334	0.844277	8.466045	0.187656	2.218808	6.152524	0.786574	4.958628	5.624834	3.572431	2.503095	3.673119	2.529943	6.805254	12.817070	0.768598	0.611263	2.190186	6.873924	1.538215	7.193319	14.639259	0.282440	1.064371	0.432235	10.652539	0.325077	53.281160	0.222154	0.041792	34.057145	9.055654	2.723166	3.181789	0.473460	0.586955	0.172871	1.515376	1.465102	12.591799	4.913267	2.023628	5.303067	8.127593	10.001850	5.320791	2.075473	3.724775	4.270243	20.643015	12.782546	17.069307	1.920769	9.971303	1.037149	1.875147	8.590383	0.404849	3.249587	0.807830	8.506834	0.329606	7.915751	2.616896	6.667235	2.059135	12.757779	0.140055	0.501138	13.355972	1.623596	1.485137	5.403863	5.513873	3.856680	0.154210	4.915514	5.047800	6.865358	1.281162	4.888821	5.539904	6.366725	2.791523	3.988214	0.049732	1.692789	3.962395	2.125105	1.905295	3.725601	3.416437	9.209425	10.788915	0.754830	3.098782	20.460665	2.557434	13.102748	1.323516	0.786665	0.159019	5.595322	5.439710	24.479820	1.519256	8.750907	13.004095	0.794706	0.766513	9.633933	2.544369	1.361697	5.238032	0.473244	2.321734	6.804848	0.379620	1.010443	9.071158	1.547617	1.280885	3.452764	0.791084	2.201934	12.654057	0.095333	0.233466	16.699938	0.019649	20.729255	0.213289	3.176656	21.337025	31.409200	0.788532	1.437016	2.443686	4.249036	1.751596	5.654759	1.033257	10.802267	5.034413	4.272290	20.967335	0.770375	0.003081	3.855533	3.560735	7.836790	3.113532	1.465594	6.186694	0.135698	0.375348	2.169809	1.857156	0.973620	0.266833	0.168607	2.011743	1.332963	1.518532	0.931416	7.298869	0.268924	1.875128	34.204085	2.481888	1.646484	1.304965	1.271213	2.106967	0.380384	0.250397	12.378059	2.454738
9768	15.486935	0.645442	3.880901	0.785463	0.574707	1.845370	0.781411	0.054348	1.318515	0.778184	1.780429	8.567200	4.264102	2.043091	1.064065	2.662971	0.780841	0.968723	24.337305	2.526721	13.435104	1.079610	0.704719	0.324624	6.158131	17.737139	4.392443	0.901697	3.642201	0.988607	0.750951	3.262859	0.658269	1.184578	1.653536	0.206987	10.557999	2.996426	0.521100	7.114413	1.846383	4.081086	0.305104	2.535013	1.863390	0.471502	1.303638	0.863251	2.253871	0.839206	1.058912	9.320358	1.523976	1.183110	1.280083	2.528665	1.425126	9.944749	1.637871	1.322167	2.934779	2.567896	0.947290	0.437253	0.640739	4.224436	6.221972	0.905835	5.731204	0.347048	19.176983	1.702412	3.474461	2.360623	2.091021	0.272402	2.505519	2.289577	0.767525	4.158224	0.416043	2.485539	8.226898	0.658200	0.630745	0.627107	2.170973	0.339146	12.316805	2.010066	0.376973	1.754955	1.553062	4.164951	0.071703	1.416624	1.666797	2.286657	1.408236	0.327799	0.954897	1.205022	2.734498	9.547216	1.179452	17.198832	6.766052	1.906439	3.491865	4.637679	4.452136	10.491130	2.041241	0.851049	1.669638	2.680982	1.737445	1.084103	2.671536	5.366304	0.238470	0.735564	1.644327	3.881857	0.680910	1.292658	0.418297	0.168429	0.527057	1.669879	3.817174	7.927835	0.312624	19.161603	1.401511	0.722931	3.475846	6.640110	0.468958	0.701217	0.958742	1.833935	8.524653	0.360939	2.867703	5.203041	0.858966	0.426105	13.399778	17.024592	0.865993	0.814246	0.650087	0.933675	2.707822	2.606150	2.139562	2.188028	12.529980	7.413671	2.220699	4.121406	5.007805	0.781751	1.158964	2.996612	1.151836	13.049745	0.364842	1.351590	1.167244	0.918296	4.793441	1.222652	1.386247	12.726185	5.627190	0.674763	3.437274	1.135515	5.514474	1.809802	0.533352	5.214058	1.501504	3.957729	0.642763	2.209487	1.886024	0.641200	2.134132	0.653376	4.003009	1.964044	0.833116	0.518383	2.602428	0.364488	0.344079	0.679825	3.515878	2.575689	0.961858	2.975553	1.592637	3.247161	0.995819	10.435009	0.672710	1.804860	0.277881	3.437988	9.098064	7.063735	3.538629	0.341889	1.311050	2.492658	1.051436	2.927473	10.611683	0.514967	1.781227	1.604999	2.150945	1.542625	0.640046	22.201300	1.353019	1.385612	2.443925	3.370951	4.016596	0.940085	4.574231	2.679636	0.517920	0.467245	1.293130	4.732177	2.433533	1.021865	0.192681	3.965480	2.973070	2.717865	0.876879	1.094640	0.932230	10.310750	2.128612	4.023091	2.687597	2.869691	0.926889	6.673972	4.882234	3.147669	23.428413	2.907250	1.092415	5.560371	5.280277	1.083822	1.053071	6.472296	0.244674	7.418085	2.218227	0.330528	0.488288	1.093495	0.496512	0.128698	0.229240	1.360681	4.429077	3.244264	0.442115	7.568829	0.693985	2.372563	8.495000	3.419468	0.967673	1.529915	1.176953	25.659550	4.207367	5.655438	1.224029	2.874640	1.603096	3.528913	2.252470	0.822739	4.314953	2.513526	5.363408	1.941361	0.596456	4.093802	2.993524	0.161496	16.234445	3.685175	0.743041	1.336226	6.619451	1.141785	1.896796	1.780789	3.531985	0.619914	17.483260	2.539468	0.752417	0.403006	1.993599	2.417770	1.355068	2.228930	2.345631	1.775536	0.432851	0.567684	1.498823	2.391393	0.986322	0.231562	2.876266	1.666013	0.683956	1.673031	0.569191	1.062540	2.710637	3.539261	3.381741	2.452749	0.664737	2.278192	1.322460	0.492254	4.034832	4.873014	9.837208	2.283074	0.369139	2.295432	15.208835	2.054960	0.577694	7.067516	7.719968	0.867394	11.294112	1.005205	1.710498	4.910564	0.556137	1.235481	1.093810	4.254378	2.365636	1.300736	0.766849	0.419258	0.243304	0.991816	2.346162	1.508358	0.242268	0.220149	0.853307	2.099055	2.010420	6.929187	0.147813	0.598453	0.306941	1.004703	0.242135	2.994615	0.861526	3.337909	1.606968	1.701487	0.633482	1.027310	6.041023	0.113756	1.153963	3.797912	2.708181	2.083189	2.753246	1.326966	0.693521	2.415917	5.018597	6.217567	0.379502	3.315905	1.153329	0.715000	0.961535	3.987237	5.701473	3.108995	3.097537	2.101348	1.298848	7.076732	5.555263	1.503833	1.165946	3.007158	1.358968	0.208444	0.968517	1.631806	1.325839	2.089567	24.360520	9.237421	30.787941	0.425989	0.353626	1.903197	1.757424	1.082075	8.229740	0.895499	0.750090	1.678324	1.960015	4.260749	10.775635	0.699447	0.467132	14.129863	6.177947	2.109128	0.850732	5.534172	3.152277	0.364390	3.349420	0.252425	0.496430	1.452531	4.318336	3.787614	1.386248	13.005300	0.790370	0.873757	0.704908	0.950203	0.833811	2.118113	1.635603	2.165838	0.697198	2.468409	0.739192	1.235631	0.409256	1.183978	0.863322	0.896428	0.776784	2.068452	1.050922	11.627182	2.036494	3.824317	4.313570	2.297102	0.524146	1.460714	2.588119	1.712888	0.512074	0.427010	4.665478	3.830143	1.500054	0.866490	2.474659	2.548482	0.392800	9.568235	2.626891	4.366804	1.634120	0.650300	1.926048	0.916992	5.817663	2.284736	6.779694	1.019170	2.533495	3.668333	20.479543	0.985185	2.870793	0.700963	6.203322	1.900922	0.137224	8.807628	1.679494	6.046061	0.553343	1.442471	11.014827	2.097438	0.444555	2.203942	2.008511	1.409400	3.563931	1.371494	0.520261	0.352632	7.096700	6.094291	0.856977	2.788389	1.704002	3.832558	1.749844	0.846150	0.844155	3.304966	2.545370	4.041681	1.287204	0.438834	18.683188	1.784100	1.240397	0.652567	0.885513	2.048938	1.588672	5.038127	1.916715	4.116867	2.469025	2.270951	0.283706	4.191760	1.070286	0.954992	1.694575	1.079678	19.651867	0.559115	9.705020	1.949647	0.812129	2.043042	1.190011	2.079108	4.465197	3.086153	2.426386	2.232860	1.649923	0.488968	1.838050	0.629535	2.892386	2.800838	0.588506	1.367229	1.802275	1.463178	0.347492	0.632336	12.122525	6.446998	5.627746	3.136985	1.184605	1.696082	2.659487	0.321430	0.140832	1.639711	0.440541	11.658327	11.542130	0.667679	0.764830	0.662573	1.148357	0.368665	2.160950	2.636780	1.660118	2.243886	3.291660	2.704538	6.249955	14.291979	0.394651	6.207207	1.429820	5.584952	6.528872	0.412533	1.033644	0.878607	0.769361	0.815373	1.150824	2.987052	2.811078	0.258161	6.075907	0.392693	1.398270	4.961230	0.905709	2.227276	23.089604	2.528806	0.658639	1.063678	1.333079	1.942396	9.304533	1.208608	1.226995	1.066325	1.114014	0.501799	3.418035	0.213673	2.209910	3.923894	1.344969	0.607181	0.832044	0.838207	1.640030	1.883841	1.629630	3.027313	0.816851	1.165096	1.159909	0.518639	0.911606	0.986025	9.425606	9.533547	4.381321	0.248083	5.870775	4.113556	0.730039	0.461984	2.569860	0.591218	5.232093	5.309647	0.591688	1.277529	3.183872	1.003797	2.531907	0.824975	4.158887	17.254406	0.536077	0.891894	2.010604	7.717377	1.547685	2.206830	0.339965	1.492019	1.966768	0.860911	2.749552	8.925660	0.104920	0.161419	4.111326	2.064634	1.120995	0.202095	1.339954	0.264328	1.919366	0.843940	0.644614	0.970767	1.731748	0.379301	6.010473	2.506097	6.050786	1.830902	0.588974	0.357701	2.260241	4.121857	4.192918	0.766895	7.448246	1.299004	1.266775	0.771557	0.990846	1.753086	0.601695	3.063064	1.477041	0.472540	2.726785	0.433179	9.057727	0.309225	0.866140	2.147753	1.619300	1.320914
9797	9.500890	2.617180	7.309690	7.674950	2.082657	7.240310	8.749535	4.119015	13.040165	5.370690	5.369805	4.854020	7.513950	9.282575	7.683540	12.781080	3.365246	7.195635	2.225414	4.890570	9.695360	4.538626	4.046284	7.920145	9.764645	14.254915	3.284662	3.719864	6.601565	5.674800	6.662725	4.407106	4.071655	7.941795	4.566425	7.608328	10.268785	3.276509	3.660110	6.342800	7.885805	16.232830	2.887713	5.888600	9.276175	5.657345	5.636245	4.901500	5.941500	6.773065	4.908645	4.064915	3.307692	6.668610	3.700974	7.477055	4.506510	7.628770	5.767000	8.783170	6.529622	6.873716	2.980908	4.446215	2.478997	5.762885	15.160765	9.883535	6.048810	4.204416	11.472950	4.412745	5.633285	6.621655	7.602220	7.483470	4.897299	8.117493	4.041522	6.174015	4.729565	12.456780	9.590340	4.052135	6.141560	4.989950	8.487070	3.431220	8.146800	6.071373	3.997665	5.047364	14.232050	2.856640	2.978880	5.226860	5.712050	8.609295	6.800155	2.845617	12.662465	7.133978	6.068075	5.032445	6.046255	3.511423	11.540595	7.731120	7.571621	11.574590	8.509930	4.028940	6.181980	5.957290	7.761170	5.222185	6.163490	4.164560	3.956370	5.275345	8.721840	3.219865	3.594145	7.697640	3.806234	5.896466	7.803675	7.821465	3.746955	7.149769	5.379655	12.405935	4.661410	19.010765	8.955125	9.329655	10.645535	11.784345	5.033080	3.274372	6.913755	5.565569	7.794025	4.319940	14.495620	12.278075	8.060650	5.965825	5.769991	9.282600	6.739805	3.145689	5.322910	7.961185	7.299885	5.874375	8.149370	9.441430	7.347793	5.790658	5.331015	5.347881	2.859295	5.796036	9.654195	5.764195	5.507600	5.038260	6.354565	2.967380	6.553365	4.157350	5.852205	7.560185	4.415460	5.499715	12.704795	8.568574	5.035431	3.514060	7.841170	7.110245	3.312075	5.944451	5.381175	6.258140	3.738514	7.204465	8.436210	5.394365	4.465844	4.033876	7.555305	6.784570	5.424615	10.045325	7.531450	3.931957	6.214425	2.413989	7.965055	4.976200	8.178045	6.266100	5.925560	6.593225	5.724500	14.242055	6.101040	5.899485	4.630874	10.720885	9.915120	15.917780	3.959685	2.755490	1.163492	3.150144	3.261030	5.208490	4.525529	5.906315	7.623810	5.242455	10.478870	6.682055	5.431115	17.795070	5.459830	9.922515	8.612511	6.574120	3.158238	4.893795	4.419120	10.804570	2.253295	8.020685	5.269035	4.425692	3.465998	6.757034	4.018795	6.480448	4.179095	10.431425	8.235785	11.286310	2.791795	5.951380	3.555410	2.950194	6.507678	7.188765	7.824860	8.352110	10.092715	4.520735	13.034340	9.217470	6.784375	7.858200	5.257955	3.262482	8.345655	10.774835	5.072639	6.659465	9.117065	13.685503	5.858205	3.250742	12.889660	3.896043	5.518075	6.079649	5.775320	3.270793	4.865485	16.818890	6.911050	6.602728	20.039500	14.841005	4.733597	7.403755	12.821895	5.606920	4.825430	13.467375	4.051061	11.634370	7.861975	5.766641	4.982111	3.457934	13.253090	7.320955	8.843380	4.415955	4.602470	8.415670	5.183054	4.590060	8.423545	7.081315	3.640820	5.387370	10.588795	5.080610	6.593235	5.138470	6.353405	4.465005	16.509007	13.810260	5.796590	5.615110	4.436185	3.927990	19.872455	8.460520	5.316235	5.576980	4.724305	3.175375	3.240064	8.055330	4.769900	4.156120	3.926996	4.967780	10.265185	7.271475	9.044475	8.149405	11.248045	10.167080	9.163315	5.735085	3.463115	12.157265	3.870490	3.327993	7.003335	6.037010	13.368730	3.964546	3.779915	14.883730	10.376815	3.939110	5.021460	5.116904	6.567225	4.711140	6.783225	4.069376	5.916280	5.215273	3.101150	3.708947	9.005935	5.054475	3.697204	7.466095	6.377870	3.273083	3.577532	5.931030	10.546730	4.512310	3.457075	4.211870	6.595395	8.774415	3.758750	6.123255	3.460524	7.409420	2.816311	6.688285	3.574268	5.555290	10.825290	5.194860	3.827300	3.364912	7.225960	2.710840	7.833820	4.859895	7.770690	10.884820	9.716425	5.542795	9.709070	7.050480	6.064060	13.197835	6.066540	9.433687	5.209330	9.069145	9.788400	4.405005	4.486550	11.333680	5.086028	4.063987	13.778155	7.825480	6.909168	6.178065	8.317575	6.264410	8.006505	3.118474	8.514500	4.551312	3.708355	8.812045	3.905903	5.342340	7.300410	15.367730	16.229125	4.548120	4.312160	2.597483	2.918790	4.407160	9.526050	2.764662	4.444381	8.198650	3.829285	9.441010	8.802215	5.842835	4.368570	5.383025	5.470604	7.313145	5.341780	14.584060	11.164330	7.296157	2.790080	6.980955	3.829189	5.814451	10.871385	9.173075	10.463910	2.978551	3.865667	5.122340	4.643640	8.864630	5.066115	7.418420	4.575070	8.173190	5.931485	5.177350	7.582325	4.855045	4.514380	4.055233	3.368720	4.358666	3.246425	8.027835	6.249620	8.352130	7.446485	3.517905	4.651223	12.588055	5.465460	6.924427	9.807474	5.435725	4.988215	4.486885	8.691880	8.447155	5.114650	4.504125	4.101772	9.183060	3.898000	10.748215	10.120285	8.975220	4.315225	4.442770	12.324993	7.561620	4.758875	3.831186	7.201040	5.521610	6.327130	3.863155	10.075285	9.823645	3.423742	6.173295	6.071495	13.439160	4.932900	8.636200	7.595715	11.681730	3.485124	5.819680	11.322860	10.173255	7.541905	5.973056	4.971883	5.903435	8.109050	4.702170	3.712625	3.674375	6.984419	8.670980	4.193492	7.654855	6.205890	6.720370	12.742925	5.144280	5.492160	7.423261	7.265600	5.816420	7.470360	4.916460	4.155755	8.703235	7.592034	10.327790	5.77249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MMRF_2499_1_BM	1.209220	1.152716	0.000000	0.256159	0.000000	0.0	1.229526	0.091957	1.483992	0.130238	0.277537	0.545370	0.282624	0.556879	0.789445	0.000000	0.641039	0.000000	0.303183	0.000000	0.705108	1.179558	0.799852	0.000000	1.276172	0.690234	0.626683	2.392130	1.170696	0.532006	0.000000	0.016584	0.014484	0.921239	0.444234	0.000000	1.301906	0.692186	1.346830	1.036520	0.957261	0.066310	0.002322	0.031481	0.078015	2.498173	0.000000	2.219191	0.000000	1.192459	0.975281	0.419098	0.007241	0.001707	0.409142	0.458742	0.629304	0.259124	0.275057	0.660593	0.691719	0.799248	0.806417	0.416159	1.039651	1.463352	0.176934	0.000000	0.009311	0.022598	0.265836	0.030723	0.387925	0.907167	0.0	0.268493	0.000000	0.000000	1.313888	0.026135	1.026417	0.804731	0.000000	0.003786	0.000000	0.002652	1.008611	0.053680	0.0	0.698493	0.702241	0.046361	0.650099	0.026630	0.393758	1.449000	0.722124	0.000000	0.000000	0.000000	0.003814	0.0	0.001756	0.481044	0.0	1.236318	0.250255	0.801686	0.846836	0.767580	0.904178	0.838343	0.816010	0.857349	1.041167	1.956709	0.0	0.647607	0.000000	0.00000	1.320074	0.000000	0.434583	0.435107	0.000000	0.343783	0.000000	0.000000	0.000000	0.001372	0.000000	0.000000	1.126865	0.427663	0.000000	1.544604	0.068257	0.553940	1.185066	0.072351	1.258534	1.331051	0.899030	0.547168	0.050792	0.058080	0.739107	0.206542	0.640599	1.296869	0.003602	0.313393	0.023939	0.473785	0.000000	1.006017	0.959900	0.431279	1.957871	0.010284	0.694572	1.337409	0.004043	0.027454	1.548861	1.092756	0.009880	0.000000	0.113847	0.004246	0.0	0.124932	0.000000	0.000000	0.0	0.003555	0.000000	1.174510	0.00000	0.000000	1.058999	0.005741	0.002381	1.643125	1.038111	0.389143	0.019761	0.521231	0.648736	0.000000	0.000000	0.000000	0.000000	0.889924	0.570902	0.067700	0.014954	0.042336	0.053314	0.474690	0.144271	0.965363	1.655038	2.122678	1.436738	1.421785	0.138564	0.656764	1.652916	1.155788	1.445173	1.053578	0.000000	0.574914	0.014993	0.009777	0.272444	0.694823	0.015522	0.090080	0.038915	0.076201	0.045630	0.300603	0.0	0.002272	1.346290	1.306071	0.944888	0.679881	0.641075	0.229158	0.043281	1.570259	0.006391	0.000000	0.0	0.000337	0.0	1.904539	0.254627	0.017069	0.0	0.011121	0.000000	2.264083	0.000000	0.225077	1.592311	0.000000	0.280450	0.005655	0.000000	0.046339	0.0	1.526803	0.004806	0.0	0.000000	0.005398	0.247718	0.003055	0.015577	1.178292	1.242034	0.263732	1.208659	1.904090	1.111323	1.610039	0.055945	1.211897	1.290489	0.147920	0.166324	1.752952	2.048213	1.007588	0.392859	0.911669	0.639023	1.339789	0.990095	0.011323	1.566615	1.679943	0.000000	1.469781	1.252064	0.006383	0.920645	0.221720	0.473050	0.438218	0.013181	0.395882	0.986326	1.558195	0.568765	0.444114	0.194751	0.0	0.00000	0.000000	0.003542	0.000950	0.322213	1.644045	0.113651	0.030121	0.084854	0.055090	0.000000	0.915113	0.036854	1.113816	1.165814	0.015502	0.858548	0.847824	0.000000	0.021413	0.047827	0.820089	1.790550	2.305796	1.065318	1.335586	1.067045	0.000000	1.472660	1.273693	0.001605	0.015682	0.000000	0.000000	0.762652	0.000000	0.915475	0.034133	0.012608	1.136111	1.801893	0.780181	0.004444	0.000753	0.366494	0.000000	0.000000	1.951881	0.785522	1.898344	1.488578	1.559518	1.349779	1.571534	1.665993	2.124990	1.631210	1.930420	1.757346	0.724368	0.000000	0.879257	1.454485	0.754386	1.282414	1.607766	0.220738	1.085961	1.401182	0.149672	1.669877	0.080613	0.905200	0.837040	0.000000	0.897979	1.710293	0.000000	0.000000	0.000000	0.022641	0.743644	0.048010	0.121167	3.935842	0.004542	1.219629	1.117278	1.355204	0.066708	0.304312	0.107940	0.660674	0.021095	0.386449	1.327332	0.825784	0.588791	0.377865	0.789651	0.002074	0.991749	0.753590	2.566005	1.187058	0.803596	1.004544	0.749753	0.811956	0.367440	0.027042	0.661649	2.286436	0.000000	0.035120	1.056707	1.092952	0.000000	0.787502	0.003518	0.000000	0.025133	0.235858	0.005000	0.053696	0.014643	0.366574	0.000000	0.961423	0.878053	0.751118	1.263447	0.249264	0.307996	0.682813	7.401997e-03	0.993629	1.503190	0.0	2.017905	0.078239	0.001376	0.000846	0.069474	0.006200	1.776679	0.000000	0.463651	0.094143	0.000872	0.731382	0.004057	0.592371	0.405741	1.245648	0.372516	0.901150	0.000000	1.487314	0.0	0.606097	0.312218	0.035654	0.303954	0.624414	0.530599	0.0	1.272046	1.756510	0.000000	1.850675	0.230083	1.693310	0.094579	1.010837	0.678758	0.256591	2.132039	1.927020	1.179046	0.289748	0.375365	0.215368	0.261853	1.227948	1.311762	0.755898	2.023968	2.074356	1.988853	1.102478	0.114571	0.966159	0.158706	0.413968	1.159796	0.971925	0.038089	0.005098	0.139404	0.207094	0.358430	0.000000	0.092257	0.0	0.000000	0.020924	0.426336	0.007174	1.388508	0.746078	0.415584	0.105177	0.108248	2.076367	0.022191	1.126657	0.742221	1.209786	1.021394	1.263844	0.000000	0.313878	0.226103	0.003006	0.031258	0.000000	0.000000	0.000000	0.052375	0.003291	0.572529	0.001506	0.013071	0.000000	0.009828	0.006006	0.000692	0.015036	0.000000	0.572500	0.047998	0.211162	0.059234	0.170686	0.065104	0.000000	1.014266	0.580142	0.061039	0.000000	0.0	0.011298	0.172596	0.005438	0.025207	1.898444	0.000000	1.987872	1.300395	0.025662	0.000000	0.006307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MMRF_1777_1_BM	1.422450	0.953137	0.000000	0.329475	0.000000	0.0	1.193723	0.105886	1.476355	0.078555	0.151009	0.321801	0.337834	0.462417	1.096079	0.000000	0.528195	0.033982	0.188736	0.008814	0.266725	1.139155	0.249709	0.002923	1.114571	0.446405	0.475001	2.389575	1.177663	0.531470	0.000000	0.002627	0.060056	0.941518	0.505347	0.000000	1.306963	0.660271	1.332139	0.975786	0.958338	0.024169	0.011133	0.017932	0.091241	2.276243	0.000000	2.037821	0.000000	1.296261	0.919932	0.248741	0.005671	0.018477	0.501748	0.347894	0.255014	0.169485	0.169122	0.785796	0.393410	0.918561	0.585775	0.422867	0.895065	1.296015	0.211674	0.000000	0.003116	0.001439	0.531480	0.023099	0.265517	0.705741	0.0	0.257759	0.000000	0.000000	1.084147	0.027753	0.782712	0.506199	0.002794	0.004600	0.000000	0.000000	0.956313	0.028905	0.0	0.678487	1.111847	0.052395	0.846472	0.010927	0.382996	1.399509	0.710731	0.002769	0.000000	0.003951	0.004694	0.0	0.012752	0.545556	0.0	1.043252	0.137656	0.899820	0.774599	0.841171	0.865587	0.828510	0.699537	0.690205	0.306519	1.875312	0.0	0.634680	0.001860	0.00000	1.381729	0.000000	0.400421	0.306828	0.000000	0.177283	0.000000	0.000000	0.000000	0.001014	0.000000	0.000000	1.343219	0.055305	0.000000	1.355068	0.031146	0.646518	1.339732	0.022989	1.170047	1.291733	0.907923	0.714696	0.111677	0.035354	0.633750	0.224419	0.571561	0.736722	0.015188	0.550528	0.058003	0.507601	0.000000	0.982153	0.839996	0.247035	2.052675	0.044335	0.439603	1.139866	0.007455	0.034002	1.085715	0.299607	0.020742	0.165675	0.082210	0.000000	0.0	0.036405	0.000000	0.000000	0.0	0.002567	0.000000	1.209016	0.00000	0.000000	0.795662	0.341891	0.000000	1.496134	1.140627	0.269728	0.040066	0.642267	0.519006	0.005984	0.005984	0.000000	0.000000	0.756322	0.402272	0.021399	0.000689	0.040819	0.011975	0.021812	0.049900	1.060532	1.649223	2.226994	1.290518	1.610135	0.054159	0.535254	1.534379	1.464106	1.323064	1.085455	0.000000	0.457564	0.038808	0.023732	0.127427	1.064915	0.006815	0.099740	0.018202	0.077512	0.217458	0.325187	0.0	0.000000	1.343519	1.369984	0.762480	0.988823	0.577850	0.103515	0.254663	1.154719	0.038577	0.000000	0.0	0.000251	0.0	1.824098	0.117250	0.009242	0.0	0.003237	0.026855	1.985354	0.000000	0.262842	1.589822	0.000000	0.133883	0.020207	0.000000	0.043228	0.0	1.468004	0.000000	0.0	0.000000	0.019549	0.010817	0.004580	0.009862	1.080305	1.326356	0.284592	0.694226	1.919469	1.013828	1.847211	0.027875	1.148723	1.077379	0.029032	0.150442	1.763210	2.017851	0.457446	0.243701	0.807067	0.729289	1.383543	0.706134	0.030329	1.588201	1.242857	0.000000	0.946563	1.251638	0.000000	1.065639	0.362079	0.424998	0.322478	0.017174	0.245287	0.804197	1.466144	0.550544	0.302932	0.057266	0.0	0.00000	0.000000	0.033081	0.009404	0.144563	1.577660	0.024960	0.006140	0.029840	0.027273	0.000000	0.914835	0.055954	1.102087	1.798157	0.030782	0.718106	1.249920	0.020367	0.006690	0.015865	0.679207	0.936092	2.481573	1.187154	1.143199	0.600862	0.000000	1.580444	1.132436	0.000000	0.066395	0.000000	0.000000	0.598669	0.003492	1.028790	0.054208	0.024428	1.319221	1.566540	0.533256	0.001660	0.000000	0.317091	0.000000	0.000000	2.172787	0.312825	1.910019	1.537391	1.751621	1.557886	1.630974	1.766620	2.037841	1.817962	2.020444	1.989493	0.702508	0.000000	0.769258	1.282216	0.643441	1.133638	1.616689	0.112529	0.795181	1.365716	0.150987	1.920813	0.079600	0.774367	0.493531	0.000000	0.887934	1.687061	0.000000	0.000000	0.000000	0.011813	0.537529	0.201247	0.110438	3.923333	0.001666	0.723893	0.948166	1.295090	0.039269	0.188242	0.323322	1.071900	0.009169	0.101776	1.311152	0.668842	0.501359	0.417975	0.910788	0.006050	1.317903	0.708876	0.635740	0.887467	0.238145	1.224292	0.604526	0.503627	0.154886	0.009610	0.466546	2.024781	0.043043	0.093029	0.826947	1.081948	0.002200	0.779756	0.145933	0.000000	0.001950	0.476579	0.007275	0.129714	0.006712	0.391320	0.000000	0.621884	0.925761	0.713453	1.502487	0.635506	0.405792	0.804700	3.195643e-08	0.948591	1.381703	0.0	1.589105	0.124386	0.000000	0.000000	0.456820	0.019887	1.496739	0.000000	0.846195	0.112595	0.000648	0.241940	0.000000	0.906640	0.595423	1.147432	0.694230	0.752286	0.001401	0.852915	0.0	0.621447	0.278815	0.029498	0.116229	0.472076	0.376648	0.0	0.995722	1.140681	0.000000	2.135740	0.044050	1.778835	0.010405	1.067532	0.319502	0.061301	2.272239	1.204587	1.230492	0.024846	0.263799	0.113800	0.721520	1.178896	1.589701	0.508399	1.848718	1.906253	1.787563	0.348165	0.101308	1.328583	0.127175	0.368869	1.957317	1.988377	0.008302	0.101576	0.139466	0.074098	0.210688	0.000000	0.057950	0.0	0.000000	0.160188	0.274114	0.001367	1.544319	0.781778	0.585818	0.047968	0.141194	2.266154	0.017971	1.175677	0.403091	1.174865	1.198932	1.421694	0.001997	0.192394	0.159015	0.012935	0.000000	0.028478	0.000000	0.010279	0.016440	0.000000	0.642529	0.000000	0.000970	0.000000	0.015928	0.012231	0.005118	0.020583	0.000000	0.302177	0.000000	0.111952	0.023044	0.294581	0.050391	0.003142	1.064503	0.849210	0.024824	0.000000	0.0	0.023679	0.084568	0.012119	0.009479	1.593339	0.000000	2.350514	1.586277	0.021615	0.000000	0.034851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MMRF_1380_2_BM	1.623038	0.567885	0.001529	0.444448	0.000000	0.0	1.617642	0.087202	1.544937	0.164772	0.784642	0.212975	0.218185	0.982978	1.387909	0.006903	0.885020	0.002653	0.570944	0.003911	0.378082	1.370940	1.029366	0.000000	1.667822	0.792543	0.795145	2.645880	1.589511	0.645718	0.000000	0.003828	0.045229	1.128383	0.868693	0.000000	1.651150	1.016860	1.796945	1.324714	1.579603	0.113220	0.022762	0.068133	0.118941	2.523347	0.000000	2.586486	0.000000	1.521805	1.349268	0.303997	0.061359	0.125937	0.870879	0.873791	0.675068	0.123753	0.060314	0.876058	0.639729	1.328133	1.509944	0.572791	1.391500	1.928610	0.107283	0.000000	0.000981	0.008587	0.724495	0.611316	0.454456	1.485805	0.0	0.057213	0.000000	0.000000	1.713937	0.037493	1.346558	0.862405	0.000000	0.072569	0.007683	0.000000	1.376393	0.075332	0.0	1.071691	2.345538	0.005892	1.043020	0.020892	0.674182	1.911910	0.975770	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	1.332137	0.0	1.853669	0.156118	0.947282	1.191515	1.172165	1.221982	1.147556	1.080038	1.363742	0.680895	2.186479	0.0	1.146940	0.000000	0.00000	1.247207	0.000000	0.789921	0.662744	0.000000	0.666395	0.000000	0.006030	0.000000	0.000000	0.004894	0.000000	1.306101	0.143687	0.000000	0.991174	0.135115	0.753079	1.529111	0.161012	1.423464	1.626946	1.200872	1.061241	0.502575	0.082877	0.897164	0.679044	0.589983	1.212116	0.005096	0.493303	0.250246	0.378574	0.000000	1.317468	1.047847	0.744241	2.635002	0.299596	0.968530	1.366772	0.001001	0.039413	0.517509	0.923603	0.000000	1.210784	0.073116	0.011660	0.0	0.156878	0.000000	0.000000	0.0	0.000000	0.009534	1.280084	0.00000	0.000000	1.407037	0.588567	0.000000	1.284192	1.765501	0.767384	0.033388	1.410792	0.827338	0.000000	0.037394	0.375850	0.000000	1.199094	0.924131	0.254764	0.003323	0.169201	0.020365	0.077479	0.187787	1.437874	2.025482	2.395873	0.946941	2.406407	0.083762	0.997855	2.134209	2.192431	1.968088	1.523437	0.000000	0.406496	0.177734	0.029039	0.385624	1.016883	0.007976	0.029453	0.075333	0.312922	0.294839	0.769730	0.0	0.000000	1.608087	1.287096	1.384314	2.232015	1.014367	0.263237	0.019043	1.443455	0.015872	0.000000	0.0	0.012832	0.0	1.324879	0.015558	0.005479	0.0	0.000000	0.065474	1.483979	0.017827	0.465487	1.811218	0.000000	0.392262	0.014279	0.000000	0.161754	0.0	2.249406	0.012049	0.0	0.000000	0.013485	0.084481	0.004265	0.020581	1.413797	1.751202	0.598336	1.058608	2.257182	1.518072	2.157620	0.028588	1.546519	1.470737	0.115088	0.075198	2.283744	2.011718	0.590565	0.174711	1.355589	1.142324	1.732309	0.999977	0.029356	2.000358	2.168486	0.005499	1.578549	1.715619	0.000000	1.086388	0.708092	0.952734	0.800759	0.000000	0.156560	0.999368	1.598378	0.780626	0.566716	0.036560	0.0	0.00000	0.005306	0.074978	0.016751	0.190419	1.753834	0.033017	0.031384	0.093525	0.041168	0.000000	1.133874	0.043497	1.350531	1.345777	0.013077	0.948813	0.548609	0.011917	0.077634	0.025800	1.188186	1.556655	2.373557	1.454293	1.725033	1.354700	0.000000	1.800362	1.935918	0.005680	0.035495	0.001798	0.000000	1.609642	0.019885	1.359545	0.079914	0.547917	1.699775	1.960632	0.785919	0.000000	0.000000	1.134081	0.000000	0.000000	2.274373	0.597937	2.430477	1.993909	1.699637	1.909058	1.937193	2.020216	2.292198	1.956799	2.384495	2.305835	1.388618	0.000000	0.967929	1.712896	0.973565	1.332929	2.082793	0.336874	1.583431	1.626683	0.412052	2.288222	0.117048	1.098633	1.112025	0.001129	1.242854	2.151584	0.000000	0.000000	0.000000	0.049565	0.274835	0.159600	0.126160	4.215939	0.049594	1.284334	1.459512	1.443623	0.018404	0.587553	0.127673	1.415874	0.041773	0.481242	1.544026	0.926057	1.129384	0.071377	0.973679	0.624028	1.295308	0.726688	0.101220	1.413063	0.573100	1.808493	0.743603	0.651887	0.282624	0.061004	0.987852	2.387189	0.047385	0.153867	1.095058	1.629619	0.000465	0.658180	0.043866	0.000000	0.001317	0.430739	0.009529	0.211640	0.018647	0.853966	0.000000	1.381601	0.979700	0.968110	1.797157	0.109721	0.430470	0.882399	6.859695e-02	0.957339	1.928806	0.0	1.768506	0.080235	0.000000	0.003256	0.030581	0.041280	1.493777	0.000000	1.045804	0.414320	0.012970	0.791701	0.002600	0.882291	0.768492	1.808702	1.025676	0.823822	0.028434	1.510927	0.0	0.913995	0.700257	0.044516	0.410598	1.131388	0.320142	0.0	1.115830	2.063326	0.000000	2.167013	0.130171	2.294290	0.121330	0.746140	0.634737	0.060047	2.503442	1.400685	1.681647	0.071716	0.538598	0.316266	0.810137	0.629558	1.339859	0.525889	1.487735	1.576491	1.302906	0.642457	0.308596	0.452688	0.215815	0.197698	1.544190	1.414015	0.002922	0.024818	0.069247	0.229902	0.522200	0.000000	0.137498	0.0	0.000000	0.105771	0.414691	0.015558	1.745483	1.355085	0.824317	0.068365	0.198851	2.180404	0.006529	1.861060	0.736698	1.739273	1.657277	1.760954	0.000000	0.840450	0.273818	0.015108	0.011965	0.083603	0.000000	0.052164	0.043703	0.000000	0.838016	0.000970	0.015884	0.004315	0.004283	0.085960	0.046859	0.035498	0.000000	1.015845	0.004744	0.327015	0.092441	0.868378	0.216510	0.000000	1.494416	1.040171	0.164499	0.000000	0.0	0.000000	0.030888	0.000000	0.089371	1.965505	0.000000	2.347432	1.956215	0.082796	0.004579	0.109184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MMRF_1700_1_BM	1.550698	0.309979	0.000000	0.260426	0.008121	0.0	1.473818	0.168679	1.645591	0.332277	0.759774	0.955099	0.047515	0.498308	1.261643	0.000000	1.024514	0.024013	0.629702	0.006178	0.796514	1.226739	0.592971	0.000000	1.208181	0.091824	0.579609	2.244965	1.268583	0.597549	0.001546	0.003853	0.033759	1.165550	0.333937	0.024710	1.580961	0.781928	1.493844	1.169754	0.609191	0.629043	0.006819	0.095598	0.049791	2.964454	0.000000	2.897611	0.658727	1.574499	1.098471	0.052610	0.027681	0.009990	0.891711	0.440038	0.390716	0.379625	0.011775	0.972876	0.670949	1.196276	0.947811	0.407554	1.465925	1.738733	0.322116	0.014321	0.008922	0.000000	0.424751	0.266030	0.412380	0.911931	0.0	0.036485	0.005349	0.016064	1.507323	0.095322	0.994378	0.423595	0.000000	0.001195	0.000000	0.002609	1.074378	0.016883	0.0	0.805682	1.268901	0.009513	0.821646	0.151664	0.663617	1.489759	0.681450	0.007385	0.000000	0.002673	0.000000	0.0	0.000000	0.387548	0.0	1.425231	0.081644	1.071860	1.015745	1.265110	0.940430	0.920234	1.068074	1.046542	0.120794	2.122144	0.0	0.897388	0.000000	0.00268	1.333923	0.001259	0.615498	0.478706	0.000000	0.132081	0.006564	0.000000	0.000000	0.000000	0.042154	0.004492	1.446161	0.109216	0.000000	0.671702	0.039074	0.938314	1.512727	0.047726	1.489006	1.678986	1.156398	0.876477	0.264920	0.141059	1.033139	0.628078	0.092480	0.993562	0.000000	0.768672	0.026781	0.405114	0.000000	1.079322	1.149622	0.132754	2.062221	0.023910	0.704186	1.349344	0.011805	0.041116	2.079753	0.614826	0.006816	0.640520	0.076202	0.000000	0.0	0.021064	0.029405	0.000000	0.0	0.013809	0.000000	1.151192	0.00000	0.000000	1.040298	0.239968	0.023114	0.796119	1.192609	0.258786	0.040339	0.343905	0.688184	0.000000	0.000000	0.007080	0.000000	0.840595	0.722066	0.021847	0.011741	0.022567	0.062436	0.920516	0.133049	1.029409	2.234011	2.343314	0.458608	1.928975	0.008029	0.720659	1.776737	1.497648	1.516473	1.157055	0.002604	0.526733	0.175289	0.030137	0.177153	1.035499	0.000000	0.234654	0.130058	0.330722	0.099174	0.647736	0.0	0.001123	1.773575	1.388511	1.159543	0.895873	1.026031	0.210133	0.267788	1.738929	0.036048	0.000000	0.0	0.005607	0.0	0.838584	0.000000	0.040755	0.0	0.000000	0.024020	1.311326	0.000000	0.165478	1.845929	0.000000	0.115215	0.000000	0.000000	0.200845	0.0	0.816539	0.016297	0.0	0.000936	0.048604	0.035823	0.053790	0.052933	1.220226	1.584272	0.470475	1.687488	2.218249	0.998485	1.967442	0.085544	1.391422	1.572205	0.154988	0.244373	1.978711	1.759709	0.913914	0.458532	1.110718	0.986083	1.825555	0.989889	0.032460	1.841535	1.310872	0.018614	2.032764	1.138227	0.000000	1.216873	0.527777	0.624602	0.839906	0.041820	0.082196	0.785387	1.714449	0.858108	0.352530	0.068147	0.0	0.00000	0.004116	0.015709	0.000000	0.379108	1.551289	0.040656	0.008286	0.063441	0.026191	0.022943	0.856311	0.319847	1.468652	1.634036	0.003517	1.196276	1.922355	0.093872	0.022522	0.027563	0.614696	1.323429	2.781918	1.204334	1.459260	1.107003	0.000000	1.691934	1.399679	0.000000	0.272191	0.304053	0.006630	1.029199	0.011800	1.373886	0.000000	0.227679	1.276087	2.067755	0.772776	0.006436	0.007938	0.997957	0.000000	0.000000	2.135346	0.085914	1.884326	1.606060	1.709466	1.482545	1.653371	1.768742	2.090022	1.733167	2.105258	1.806784	0.785157	0.012915	0.906082	1.639234	0.998024	1.447474	1.780278	0.061169	1.152331	1.912038	0.318241	1.723121	0.047001	0.942589	0.978133	0.498774	1.218184	1.954793	0.000000	0.106654	0.038934	0.188345	0.163750	1.039095	0.089976	3.972707	0.017610	1.445026	1.113409	1.309945	0.335283	0.412071	0.032165	0.939284	0.007036	0.484451	1.491678	0.694886	1.021581	0.105465	1.152441	0.002041	1.395513	0.889409	1.705610	1.219996	0.384534	1.443493	0.746366	1.553906	0.751633	0.015225	0.995323	2.204923	0.049642	0.100246	1.441395	1.162149	0.002107	0.808181	0.251774	0.399331	0.025308	0.963524	0.067898	0.139957	0.000000	0.385567	0.000000	0.658390	1.121803	1.320479	1.760206	0.647822	0.606364	0.787487	1.590295e-02	0.739378	1.335560	0.0	1.627437	0.370004	0.012044	0.000834	0.713344	0.002743	1.827707	0.002417	0.778714	0.432031	0.016883	0.594270	0.000000	0.847437	0.667622	1.570859	0.735267	0.958421	0.007465	0.972042	0.0	0.515805	0.259084	0.013546	0.311824	0.813924	0.293069	0.0	1.294698	1.183976	0.025514	2.309971	0.015152	1.823010	0.104494	0.760309	0.456026	0.467149	2.218685	1.425042	1.430278	0.008475	0.446894	0.513298	0.635688	1.314973	1.518987	0.240298	1.154016	1.261225	1.182075	2.127995	0.753584	2.137810	0.000000	0.098273	2.794627	2.770901	0.021997	0.009947	0.418802	0.218804	0.126102	0.039683	0.008799	0.0	0.000000	0.144081	0.495761	0.184482	1.674389	1.108694	0.693838	0.024017	0.122784	2.216417	0.111525	1.759635	0.737524	1.327118	1.021067	1.325733	0.010382	0.245907	0.257974	0.161568	0.021471	0.014527	0.000000	0.018715	0.183107	0.000000	0.959158	0.000743	0.027635	0.037822	0.107179	0.011549	0.083077	0.217491	0.000000	0.512272	0.129739	0.140442	0.055270	0.104009	0.102575	0.004307	1.102259	0.685229	0.103924	0.051359	0.0	0.034701	0.180898	0.001345	0.004039	1.912713	0.000000	2.251641	1.550618	0.005780	0.001799	0.151953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MMRF_1918_1_BM	0.680025	0.071806	0.000000	0.509063	0.000000	0.0	1.603720	0.086551	1.821278	0.393870	0.556723	0.411699	0.235314	0.873423	1.162397	0.016206	0.791693	0.002324	0.659484	0.013336	1.011348	1.275145	1.170262	0.010426	1.562118	0.806923	0.757939	2.590762	1.366117	0.716280	0.000000	0.000000	0.015758	1.293004	0.596735	0.000000	1.512579	0.883387	1.611896	1.287979	0.689407	0.012770	0.014521	0.087280	0.029674	2.490518	0.000000	2.450726	0.000000	1.315798	1.395903	0.038746	0.002907	0.000000	0.638571	0.556101	0.744105	0.503056	0.030366	1.062368	1.046304	0.981041	0.992924	0.181115	1.678430	1.867974	0.742248	0.000000	0.004291	0.001091	0.578568	0.046055	0.527172	1.108869	0.0	0.167438	0.000000	0.000000	1.653366	0.018911	1.314354	0.615674	0.000000	0.000000	0.000000	0.072238	1.279117	0.255123	0.0	1.125836	1.207796	0.019564	0.921297	0.166841	0.380378	2.030162	1.214836	0.000000	0.000000	0.000000	0.000000	0.0	0.017429	0.465127	0.0	1.542158	0.154747	1.172510	1.271333	1.033847	1.094586	1.174900	1.271437	1.394560	1.305087	2.242303	0.0	1.097438	0.000000	0.00000	1.889046	0.000000	0.611688	0.785449	0.042655	0.451436	0.000000	0.014213	0.000000	0.001544	0.002150	0.000000	1.412603	0.107546	0.000000	0.347111	0.065920	0.411456	1.579953	0.134436	1.408215	1.595155	1.047142	0.915917	0.055542	0.084192	1.200711	0.480236	0.102297	0.992670	0.008628	0.502734	0.058172	0.635906	0.000000	1.509333	1.380463	0.505936	2.203270	0.062855	0.788388	1.075175	0.005243	0.128700	1.617532	0.762477	0.000000	0.207456	0.134255	0.000000	0.0	0.009357	0.000000	0.000000	0.0	0.007858	0.000000	1.216825	0.00000	0.000000	1.260217	0.018916	0.000000	1.722096	1.280838	0.450939	0.042569	0.033181	1.172083	0.002251	0.002251	0.000000	0.091977	1.383221	0.893816	0.113345	0.011011	0.285137	0.056262	1.298165	0.013020	1.080067	1.993348	2.178110	0.202742	1.501904	0.013084	0.778101	1.889053	1.485846	1.625849	1.169366	0.000000	0.312268	0.061706	0.037445	0.178211	1.010679	0.023460	0.004158	0.004748	0.183381	0.039997	0.520797	0.0	0.001284	1.638856	1.634774	1.238979	0.973351	1.232239	0.533800	0.000000	1.930259	0.052572	0.000000	0.0	0.000381	0.0	0.653050	0.000000	0.000000	0.0	0.000000	0.020358	0.652475	0.000000	0.084049	1.898298	0.000000	0.222934	0.006303	0.002519	0.018627	0.0	1.746384	0.000000	0.0	0.002140	0.036758	0.036638	0.000537	0.005684	1.391300	1.748328	0.603916	0.371842	2.306946	1.281978	2.047777	0.010930	1.450800	1.334468	0.167167	0.273584	1.990805	1.606215	0.790795	0.028063	0.877977	1.116399	1.958851	1.117056	0.016489	1.798823	1.968260	0.000000	1.575910	1.839145	0.000000	0.753901	0.300311	0.988053	0.569622	0.024897	0.017932	0.787047	1.775657	0.761828	0.745086	0.773000	0.0	0.00000	0.000000	0.000000	0.000000	0.227168	1.739664	0.204460	0.000000	0.015823	0.000000	0.000000	1.204538	0.017132	1.252508	1.534877	0.008013	0.735044	0.359004	0.000000	0.011067	0.018282	1.173035	0.530168	2.496397	1.528819	1.940841	1.591609	0.000000	1.573754	1.506832	0.000000	0.048806	0.000000	0.000000	0.368177	0.012801	0.961506	0.000000	0.143118	1.714379	1.976532	1.198154	0.001261	0.000850	0.526417	0.000000	0.000000	2.196475	0.959563	2.172060	1.786794	1.600180	1.590909	1.907700	1.803319	2.213167	1.872640	2.077753	1.899108	0.923438	0.000000	0.999772	1.543486	1.160954	1.569852	1.795050	0.277426	1.444440	1.820350	0.437730	1.830781	0.010233	1.189720	0.975098	0.000000	1.206216	1.889477	0.000000	0.000000	0.007653	0.053714	0.050878	2.111303	0.121100	4.081966	0.012591	1.061279	0.977369	1.500966	0.012080	0.126354	0.239800	0.878916	0.047813	0.606333	1.451591	0.904219	0.817872	0.071644	1.045741	0.314741	1.375845	1.158088	2.683792	1.382777	0.406862	1.048865	1.028422	0.352115	0.029795	0.002201	1.101799	2.388842	0.104600	0.266462	1.330702	1.437544	0.002416	1.341482	0.017191	0.000000	0.063450	0.016174	0.000000	0.153253	0.000000	0.290802	0.011349	1.226128	0.962053	0.722090	1.913950	0.497201	0.477944	0.972703	4.789335e-02	0.634440	1.075536	0.0	2.251492	0.091998	0.000000	0.000000	0.000000	0.001404	1.863677	0.000000	1.703233	0.020712	0.017848	0.884933	0.000000	1.206548	0.545202	1.600165	0.874283	1.046967	0.006387	1.321325	0.0	0.777835	0.498707	0.062265	0.581889	0.932187	0.492060	0.0	0.861626	1.846228	0.001993	2.085997	0.012017	1.884283	0.065210	1.055508	0.406230	0.572924	2.454029	2.101214	1.659100	0.503026	1.040617	0.595519	0.713598	1.229597	1.746070	0.030793	0.432124	0.526302	0.414007	0.547643	0.142523	0.128842	0.033847	0.038494	0.576142	0.546355	0.007631	0.016136	0.192656	0.430987	0.007391	0.000000	0.003887	0.0	0.000000	0.160357	0.628831	0.000000	1.656312	1.231686	0.603604	0.425169	0.279034	2.208898	0.001900	1.422547	0.714240	1.475539	1.092903	1.653920	0.000000	0.179307	0.220295	0.036449	0.000000	0.123376	0.026935	0.010610	0.020924	0.000000	0.884353	0.000000	0.000000	0.018748	0.001669	0.009186	0.000560	0.059357	0.000000	1.110977	0.021661	0.263045	0.161101	0.341120	0.111139	0.000000	1.218023	1.226633	0.018323	0.000000	0.0	0.003180	0.127515	0.003304	0.004329	1.952974	0.000000	2.449336	1.783225	0.065427	0.000000	0.015252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9	0.0	0.0	0.108939	0.037925	0.0	0.0	0.000000	0.0	0.050993	0.000	0.0	0.105772	0.000000	0.0	0.127890	0.000000	0.000000	0.000000	0.000000	0.347510	0.0	0.000000	0.0	0.112814	0.006378	0.304040	0.000000	0.0	0.082344	0.0	0.000000	0.0	0.007881	0.0	0.0	0.000000	0.108938	0.972652	0.0	0.394345	0.000000	0.0	0.000000	0.0	1.027224	0.170962	0.000000	0.041372	0.154846	0.000000	0.000000	0.000000	0.005152	0.162644	0.00000	0.0	0.000000	0.023190	0.009266	1.105353	0.0	0.0	0.200513	0.017063	0.076459	0.000000	0.101150	0.022634	0.060475	0.0	0.00000	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.219331	0.000000	0.050403	0.000000	0.0	0.000000	0.0	0.717019	0.364403	0.0	0.062520	0.374714	0.000000	0.044609	0.101290	0.000000	0.000000	0.014772	0.000000	0.000000	0.0	0.000000	0.011087	0.000000	0.009764	0.201163	0.000000	0.065925	0.0	0.000000	0.065161	0.0	0.0	0.021517	0.0	0.203000	1.033312	0.000000	0.0	0.0	0.0	0.000000	0.0	0.0	0.0	0.306376	0.000000	0.0	1.478846e-01	0.0	0.0	0.0	0.000000	0.000000	0.0	0.030259	0.0	0.000000	0.0	0.0	0.000000	0.027962	0.010069	0.130801	0.000000	0.441967	0.000000	0.000000	0.000000	0.0	0.013535	0.000000	0.000000	0.031613	0.0	0.000000	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.113505	0.000000	0.0	0.000000	0.0	0.0	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.0	0.000000	0.055916	0.578521	0.0	0.0	0.0	0.0	0.0	0.000000	0.792528	0.0	0.0	0.0	0.000000	0.000000	0.006245	0.00000	0.000000	0.033208	0.000000	0.405333	0.0	0.0	0.067502	0.006097	0.657591	0.000000	0.013154	0.580455	0.088459	0.000000	0.000000	0.046084	0.0	0.0	0.031312	0.000000	0.000000	0.014685	0.000000	0.042525	0.351941	0.0	0.000000	0.000000	0.355275	0.123486	0.720781	0.063481	0.0	0.000000	0.093855	0.000000	0.000000	0.033925	0.0	0.0	0.073870	0.0	0.343206	0.238636	0.0	0.0	0.0	0.000000	0.0	0.116092	0.157603	0.0	0.000000	0.130869	0.328661	0.000000	0.000000	0.0	0.0	0.0	0.0	0.0	0.063263	0.0	0.018832	0.000000	0.000000	0.012693	0.0	0.498763	0.0	0.017917	0.000000	0.000000	0.334279	0.000000	0.022120	0.0	0.170103	0.000000	0.000000	0.000000	0.051712	0.129881	0.000000	0.000000	0.011009	0.065422	0.000000	0.051974	0.0	0.296151	0.000000	0.0	0.0	0.0	0.031260	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.323803	0.000000	0.00000	0.0	0.019015	0.130049	0.085297	0.144484	0.118960	0.000000	0.323264	0.009962	0.040189	0.000000	0.000000	0.022288	0.000000	0.000000	0.0	0.000000	0.0	0.0	0.0	0.000000	0.060614	0.012366	0.0	0.041796	0.086265	0.000000	0.0	0.061240	0.000000	0.340400	0.100115	0.376931	0.027905	0.116802	0.0	0.051087	0.128584	1.446879	0.116498	0.021206	0.000000	0.0	0.219835	0.0	0.187062	0.0	0.0	0.339261	0.000000	0.020263	0.000000	0.0	0.0	0.000000	0.0	0.0	0.392152	0.00000	0.0	0.000000	0.023813	0.0	0.000000	0.0	0.088061	0.000000	0.100951	0.000000	0.145156	0.0	0.0	0.0	0.0	0.0000	0.0	0.0	0.000000	0.160593	0.0	0.0	0.0	0.010857	0.000000	0.0	0.000000	0.0	0.042908	0.000000	0.000000	0.000000	0.0	0.000000	0.00000	0.586689	0.000000	0.01811	0.0	0.000000	0.0	0.000000	0.0	0.0	0.000000	0.0	0.000000	0.000000	0.0	0.010610	0.0	0.578738	0.0	0.000000	0.0	0.0	0.064348	0.0	0.0	0.0	0.000000	0.000000	0.0	0.000000	0.000000	0.017572	0.000000	0.000000	0.046986	0.0	0.000000	0.055760	0.087143	0.613680	0.000000	0.0	0.092732	0.0	0.000000	0.000000	0.0	0.0	0.000000	0.0	0.0	0.016256	0.0	0.016353	0.0	0.005853	0.000000	0.000000	0.000000	0.422360	0.0	0.059976	0.0	0.037383	0.0	0.000000	0.0	0.0	0.000000	0.0	0.245439	0.000000	0.000000	0.0	0.000000	0.0	0.000000	0.000000	0.0	0.047872	0.000000	0.044883	0.435905	1.123208	0.0	0.000000	0.000000	0.021304	0.000000	0.428083	0.086942	0.040308	0.000000	0.002444	0.148796	0.156807	0.0	0.0	0.007410	0.000000	0.260435	0.0	0.215345	0.0	0.000000	1.081747	0.000000	0.217464	0.0	0.0	0.000000	0.0	0.000000	0.393996	0.000000	0.0	0.0	0.000000	0.000000	0.0	0.0	0.000000	0.040926	0.376831	0.0	0.0	0.000000	0.0	0.00000	0.000000	0.055979	0.000000	0.000000	0.000000	0.000000	0.161479	0.062514	0.000000	0.089494	0.195888	0.233113	0.0	0.0	0.406438	0.000000	0.000000	0.278334	0.469482	0.131103	0.000000	0.097218	0.0	0.000000	0.0	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.000000	0.416559	0.178704	0.114732	0.0	0.257495	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.024612	0.0	0.0	0.000000	0.000000	0.0	0.482618	0.216586	0.0	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.069393	0.028958	0.0	0.0	0.054351	0.056296	0.000000	0.000000	0.459430	0.000000	0.0	0.000000	0.004685	0.0	0.0	0.0	1.167999	0.0	0.000000	0.000000	0.000000	0.054592	0.085590	0.0	0.000000	0.0	0.000000	0.0	0.094699	0.059173	0.0	0.000000	0.000000	0.000000	0.0	0.083337	0.009697	0.000000	0.0	0.0	0.089228	0.0	0.813046	0.695018	0.0	0.000000	0.000000	0.762280	0.048019	0.021139	1.477640	0.549314	0.354605	0.0	0.0	0.095812	0.682178	0.125196	0.141550	1.697935	1.245592	0.000000	0.0	0.0	0.016751	0.000000	0.799314	0.634961	0.0	1.111207	0.074084	0.000000	0.814866	0.000000	0.048746	1.463209	0.000000	0.146349	0.168532	0.017192	1.068881	0.102275	1.197261	0.269226	1.487616	0.775576	0.091768	0.668563	0.537172	0.105945	0.006238	0.499280	0.030685	0.0	0.0	0.0	0.661835	0.288539	0.928319	0.019509	0.001833	1.164049	0.013463	0.033153	0.0	0.599151	0.409299	0.251402	0.806202	0.723558	0.000000	0.000000	0.034212	0.023990	0.000000	0.037967	0.706218	0.000000	0.227169	0.051420	0.000000	0.544822	0.346577	0.316019	1.205963	1.369578	0.000000	1.292055	1.632483	0.0	0.035244	1.217859	0.564729	0.655016	1.760496	0.311414	0.810034	0.551243	0.828888	1.235517	1.513169	0.602658	1.393980	0.010758	0.392882	0.0000	0.040331	0.657945	0.581387	0.003512	0.125355	2.745011	0.005059	0.0	1.224887	0.521327	0.737764	0.540662	0.204878	0.372340	1.254503	1.260236	0.000000	0.129045	0.756748	0.097428	0.341143	0.348660	1.365206	0.304319	0.666010	2.125810	0.806302	0.007399	0.824633	0.000000	0.003448	0.581236	0.776952	0.617971	1.087825	0.0	0.0	0.464288	0.705491	1.001590	0.000000	0.232133	1.595356	1.341331	0.543291	0.538415	0.365659	0.875551	TRUE

	classifier	accuracy_mean	accuracy_sd	precision_mean	precision_sd	recall_mean	recall_sd	AUC	time_s
0	RF	0.617	0.027	0.603	0.060	0.312	0.070	0.650	15.425
1	KNN	0.595	0.035	0.550	0.088	0.367	0.043	0.596	17.609
2	ADA	0.597	0.022	0.537	0.041	0.473	0.036	0.603	230.223
3	BAG	0.617	0.012	0.611	0.037	0.412	0.083	0.662	918.870
4	NB	0.524	0.037	0.447	0.050	0.471	0.075	0.528	3.937
5	CART	0.589	0.028	0.525	0.024	0.568	0.084	0.583	28.297
6	ID3	0.571	0.046	0.476	0.069	0.486	0.047	0.550	37.043
7	ST	0.589	0.030	0.520	0.041	0.745	0.090	0.611	5.846
8	MLP	0.571	0.018	0.000	0.000	0.000	0.000	0.500	37.658

	predicted
MMRF_2062_1_BM	1
MMRF_1328_1_BM	0
MMRF_1850_1_BM	0
MMRF_1856_1_BM	0
MMRF_1744_1_BM	0
...	...
MMRF_1451_1_BM	1
MMRF_1107_1_BM	1
MMRF_1461_1_BM	1
MMRF_2377_1_BM	1
MMRF_1189_1_BM	0

	D_Age	D_Gender	D_OS	D_OS_FLAG	D_PFS	D_PFS_FLAG	RNASeq_geneLevelExpFileSamplId	CYTO_predicted_feature_01	CYTO_predicted_feature_02	CYTO_predicted_feature_03	CYTO_predicted_feature_05	CYTO_predicted_feature_06	CYTO_predicted_feature_08	CYTO_predicted_feature_12	CYTO_predicted_feature_13	CYTO_predicted_feature_14	CYTO_predicted_feature_15	CYTO_predicted_feature_16	CYTO_predicted_feature_17	CYTO_predicted_feature_18	HR_FLAG
0	54	Female	1575	0	623	1	MMRF_1021_1_BM	0	0	1	0	1	0	0	0	0	0	0	0	0	FALSE
1	46	Male	995	0	995	0	MMRF_1029_1_BM	0	0	1	0	0	0	0	0	0	0	0	0	0	FALSE
2	65	Female	1572	0	1572	0	MMRF_1030_1_BM	0	0	1	0	0	1	0	0	0	1	0	0	0	FALSE
3	62	Male	1581	0	1329	1	MMRF_1031_1_BM	0	0	1	0	0	0	0	0	0	0	0	0	0	FALSE
4	76	Male	934	1	908	1	MMRF_1032_1_BM	0	0	1	0	0	1	0	0	0	0	0	0	0	FALSE
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
578	67	Male	335	0	335	0	MMRF_2608_1_BM	0	0	1	0	0	1	0	0	0	0	1	0	0	TRUE
579	66	Female	247	0	246	0	MMRF_2611_1_BM	0	0	1	1	0	1	0	1	0	0	0	1	0	TRUE
580	70	Female	253	0	253	0	MMRF_2613_1_BM	0	0	0	0	0	0	0	0	0	0	0	0	0	TRUE
581	58	Male	290	0	290	0	MMRF_2614_1_BM	1	1	0	0	1	0	1	0	0	0	1	0	0	TRUE
582	59	Male	260	0	260	0	MMRF_2622_1_BM	1	0	0	0	0	0	0	0	0	0	0	0	0	TRUE

	parameter	lasso train model score	lasso test model score
0	0.1	0.0	-0.001361
0	0.2	0.0	-0.001361
0	0.3	0.0	-0.001361
0	0.4	0.0	-0.001361
0	0.5	0.0	-0.001361
...	...	...	...
0	9.5	0.0	-0.001361
0	9.6	0.0	-0.001361
0	9.7	0.0	-0.001361
0	9.8	0.0	-0.001361
0	9.9	0.0	-0.001361